record_id,doi,title,year,venue,authors,abstract,citations,oa_pdf
4cce35e2462aa6adb0b180455af1e248a840a902,10.1109/eiecs53707.2021.9587907,House price prediction based on machine learning and deep learning methods,2021.0,2021 International Conference on Electronic Information Engineering and Computer Science (EIECS),Yihao Chen; Run-Qian Xue; Yu Zhang,"House price is closely related to everyone's life, and it is affected by many factors. House price prediction is an important research point for that it can help people to make strategies about house dealing. There are many existing researches about house price prediction. However, existing studies cannot make a comprehensive comparison and analysis of popular housing price forecasting methods. This paper uses five popular machine learning and in-depth learning approaches to predict house prices and evaluate the models. Our methods include Linear Regression (LR), Bayesian, Backpropagation neural network (BP neural network), Support Vector Machine (SVM), Deep Neural Network (DNN). The dataset we used is from the Kaggle platform. Experimental results show that Bayesian, Backpropagation neural networks, and SVM are more suitable for house price prediction.",14.0,
c8fcbc7b465576205da284b034de45a62993a206,10.1177/13548166231184792,Tourism development and housing price: An interplay,2023.0,Tourism Economics,Jun Zhang,"This study underscores the significance of the nexus between tourism and housing price. Using panel data from 35 major cities in China, it employed the panel smooth transition regression approach to explore the interplay between tourism development and housing prices. Our findings showed a nonlinear tourism–housing price nexus. Specifically, tourism development can raise housing prices in a nonlinear way, indicating that this positive impact varies at different levels of tourism specialisation. Housing prices had an inverted U-shaped effect on tourism development. Several explanations for these empirical results are provided in the Discussion section, along with policy suggestions.",14.0,
59261e2e39b0c3b4a97c6493a726202144018f9e,10.1108/ijhma-06-2015-0028,The effect of quality determinants on house prices of eight capital cities in Australia,2016.0,,Ti-Ching Peng; Chien-Fu Chen,,14.0,
50afd590e4e51a81c9afb1a5eb44b454e9f8473e,10.1068/b37062,Exploring the Ripple Effect and Spatial Volatility in House Prices in England and Wales: Regressing Interaction Domain Cross-Correlations against Reactive Statistics,2013.0,,Crispin H. V. Cooper; S. Orford; C. Webster; Christopher B. Jones,,14.0,
db7b538b454552788bf9ab1b9b23610881d1c342,10.1080/09599916.2012.717100,"Analysis of household location behaviour, local amenities and house prices in a sorting framework",2012.0,,M. van Duijn; J. Rouwendal,,14.0,
d58ecfd119a34ae0419e39082b7114991b030168,10.1109/geoinformatics.2011.5981032,Geographically Weighted Regression model (GWR) based spatial analysis of house price in Shenzhen,2011.0,GEOINFORMATICS,Ji-Jin Geng; K. Cao; Le Yu; Yong Tang,,14.0,
42b9fb5cc4a4aa5b44ce497e4194bed6bc23be94,10.1093/acrefore/9780190632366.013.44,Environmental Health Concerns From Unconventional Natural Gas Development,2019.0,Oxford Research Encyclopedia of Global Public Health,Irena Gorski; B. Schwartz,"Unconventional natural gas development (UNGD), which includes the processes of horizontal drilling and hydraulic fracturing to extract natural gas from unconventional reservoirs such as shale, has dramatically expanded since 2000. In parallel, concern over environmental and community impacts has increased along with the threats they pose for health. Shale gas reservoirs are present on all continents, but only a small proportion of global reserves has been extracted through 2016. Natural gas production from UNGD is highest in the United States in Pennsylvania, Texas, Louisiana, Oklahoma, and Arkansas. But unconventional production is also in practice elsewhere, including in eighteen other U.S. states, Canada, and China. Given the rapid development of the industry coupled with its likelihood of further growth and public concern about potential cumulative and long-term environmental and health impacts, it is important to review what is currently known about these topics.
 The environmental impacts from UNGD include chemical, physical, and psychosocial hazards as well as more general community impacts. Chemical hazards commonly include detection of chemical odors; volatile organic comp",13.0,
b9c579f2a1f76d045c3ff69bdc585943e16f00f6,10.1155/2022/9946703,Experimental Research on the Impact of Interest Rate on Real Estate Market Transactions,2022.0,Discrete Dynamics in Nature and Society,Chang-Jen Chen; Haoyu Zhai; Zhi-Ruo Wang; Shen Ma; Jie Sun; Cheng-Liang Wu; Yang Zhang,"Based on the close relationship between real estate and capital financing, this paper specifically discusses the impact of interest rate changes in China on real estate market transactions. Housing price regulation has been a hot and focused topic of economics research. It is important to explore the effect of interest rate on real estate market transactions, for the interest rate is an important tool for the government to carry out macrocontrol of the real estate market and affects residents’ consumption choices of goods. Currently, most scholars believe that interest rate changes have a significant impact on house prices, but their studies are often based on empirical analysis of macro data, making it difficult to exclude the effects of economic differences, geographic characteristics, and other factors. Also, some scholars believe that it is difficult for interest rates to have a long-term impact on real estate prices. This paper applies the experimental economics method to simulate real estate transactions in the real market and control the irrelevant variables to study the movements of housing prices, transaction volumes, consumer transaction intentions and bubble indices unde",13.0,https://downloads.hindawi.com/journals/ddns/2022/9946703.pdf
2798d1fb4dd4d5e8d54866355f1b51f6b87a9c43,10.1136/bmjopen-2020-040664,"Ideal cardiovascular health in urban Jamaica: prevalence estimates and relationship to community property value, household assets and educational attainment: a cross-sectional study",2020.0,BMJ Open,Joette A McKenzie; N. Younger; M. Tulloch-Reid; I. Govia; N. Bennett; Shelly R. McFarlane; Renee Walters; Damian K. Francis; K. Webster-Kerr; A. Grant; T. Davidson; R. Wilks; David R. Williams; T. Ferguson,"Objective Ideal cardiovascular health (ICH) is associated with greater longevity and reduced morbidity, but no research on ICH has been conducted in Jamaica. We aimed to estimate the prevalence of ICH in urban Jamaica and to evaluate associations between ICH and community, household, and individual socioeconomic status (SES). Design Cross-sectional study. Setting Urban communities in Jamaica. Participants 360 men and 665 women who were urban residents aged ≥20 years from a national survey, the Jamaica Health and Lifestyle Survey 2016–2017. Exposures Community SES, using median land values (MLV); household SES, using number of household assets; and individual SES, using education level. Primary outcome The main outcome variable was ICH, defined as having five or more of seven ICH characteristics (ICH-5): current non-smoking, healthy diet, moderate physical activity, normal body mass index, normal blood pressure, normal glucose and normal cholesterol. Prevalence was estimated using weighted survey design and logistic regression models were used to evaluate associations. Results The prevalence of overall ICH (seven characteristics) was 0.51%, while the prevalence of ICH-5 was 22.9% (m",13.0,https://bmjopen.bmj.com/content/bmjopen/10/12/e040664.full.pdf
e2bbd1830ab2ddb1b60e132bc7389b7b675579ce,10.1007/s11146-024-10002-7,Towards a Better Uncertainty Quantification in Automated Valuation Models,2024.0,Journal of real estate finance and economics,A. J. Pollestad; Arild Brandrud Næss; A. Oust,"This study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimat",13.0,https://link.springer.com/content/pdf/10.1007/s11146-024-10002-7.pdf
9d9c36fb0e4e7b980c860f7e6515ef6d805391ab,10.1088/1755-1315/1342/1/012030,A review of sustainable housing preferences and affordability,2024.0,IOP Conference Series: Earth and Environment,S. Mushanga; O. Oloke; D. O. Olukanni,"The review examined the potential connections between sustainable housing, and sustainable affordability of such housing while meeting the housing preferences of various households. It is widely known that many lower-income countries are facing a housing crisis, and it is crucial to address this issue by providing affordable housing that meets individual needs while also promoting environmentally friendly living. This review paper Is centered on the research question: How can sustainable housing be made more affordable and accessible to all households while meeting their housing preferences? A qualitative study of 66 publications from 2019 to September 2023 found that sustainable housing offers ecological and energy-efficient benefits, but there are barriers to scaling up these models, including economic, cultural, and legal challenges. Housing preferences are influenced by factors such as cost, location, and amenities, with affordability being a significant concern. While environmental and economic sustainability can positively impact housing prices in turn affect the affordability of such housing, the initial investment costs can be challenging for lower-middle-income households.",13.0,https://doi.org/10.1088/1755-1315/1342/1/012030
5e83261e49e7715cfa061ba4d497b1dab4327631,10.1108/ijhma-10-2023-0145,Assessing the effect of housing attributes and green certification on Malaysian house price,2024.0,International Journal of Housing Markets and Analysis,Nor Nazihah Chuweni; N. Fauzi; Asmma' Che Kasim; Sekar Mayangsari; N. Wardhani,"
Purpose
Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices.


Design/methodology/approach
The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result.


Findings
The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Buil",13.0,
93592e65a1eb94428d33a98654a11bb888a8ce6c,10.1016/j.landurbplan.2021.104060,Proximity to freshwater blue space and type 2 diabetes onset: the importance of historical and economic context,2020.0,Landscape and Urban Planning,M. Poulsen; B. Schwartz; Joseph DeWalle; C. Nordberg; J. Pollak; Jennifer Silva; Carla I. Mercado; Deborah B. Rolka; Karen R. Siegel; Annemarie G. Hirsch,"Salutogenic effects of living near aquatic areas (blue space) remain underexplored, particularly in non-coastal and non-urban areas. We evaluated associations of residential proximity to inland freshwater blue space with new onset type 2 diabetes (T2D) in central and northeast Pennsylvania, USA, using medical records to conduct a nested case-control study. T2D cases (n=15,888) were identified from diabetes diagnoses, medication orders, and laboratory test results and frequency-matched on age, sex, and encounter year to diabetes-free controls (n=79,435). We calculated distance from individual residences to the nearest lake, river, tributary, or large stream, and residence within the 100-year floodplain. Logistic regression models adjusted for community socioeconomic deprivation and other confounding variables and stratified by community type (townships [rural/suburban], boroughs [small towns], city census tracts). Compared to individuals living ≥1.25 miles from blue space, those within 0.25 miles had 8% and 17% higher odds of T2D onset in townships and boroughs, respectively. Among city residents, T2D odds were 38-39% higher for those living 0.25 to <0.75 miles from blue space. Resi",13.0,https://doi.org/10.1016/j.landurbplan.2021.104060
5a2ed8dc0bb646f525f64160627b03806df62047,10.1016/j.scitotenv.2020.144605,"Optimization of residential green space for environmental sustainability and property appreciation in metropolitan Phoenix, Arizona.",2020.0,Science of the Total Environment,Chu-Yuan Wang; V. Turner; E. Wentz; Qunshan Zhao; S. Myint,"Cities in arid and semi-arid regions have been exploring urban sustainability policies, such as lowering the vegetation coverage to reduce residential outdoor water use. Meanwhile, urban residents express concerns that such policies could potentially impact home prices regardless of the reduced water costs because studies have shown that there is a positive correlation between vegetation coverage and home values. On the other hand, lower vegetation coverage in arid and semi-arid desert regions could increase surface temperatures, and consequently increases energy costs. The question is therefore where the point in which residential outdoor water use can be minimized without overly increasing surface temperatures and negatively impacting home values. This study examines the impacts of spatial composition of different vegetation types on land surface temperature (LST), outdoor water use (OWU), and property sales value (PSV) in 302 local residential communities in the Phoenix metropolitan area, Arizona using remotely sensed data and regression analysis. In addition, the spatial composition of vegetation cover was optimized to achieve a relatively lower LST and OWU and maintain a relat",13.0,https://doi.org/10.1016/j.scitotenv.2020.144605
64e3d8db3e5d8873b1c072bc1e9bfe178b55149a,10.1177/2399808320951212,Shedding new light on residential property price variation in England: A multi-scale exploration,2020.0,Environment and Planning B Urban Analytics and City Science,B. Chi; A. Dennett; Thomas Oléron-Evans; R. Morphet,"Exploring the nature of spatial and temporal variation in house prices is important because it can help better understand such issues as affordability and equity of access to housing. In the UK, research on house price variation has been hindered by a lack of extensive data linking the prices of properties at different places and times to their physical attributes. This paper addresses this gap through using a new dataset linking Land Registry Price Paid Data to attribute data from Ordnance Survey and Energy Performance Certificates datasets. The new data are used to investigate spatial disparities in England’s house prices at four geographical scales (from local authority to individual address) between 2009 and 2016 – a period of sustained price rises after the global financial crisis of 2008. We selected two housing price measures for comparison, namely transaction price and the house price per square metre. Multilevel variance components models are used to estimate variation in the two house price measures at four different spatial scales and we compare spatial disparities in the two measures at these different scales. Our results suggest that accounting for the size of properti",13.0,https://journals.sagepub.com/doi/pdf/10.1177/2399808320951212
804ce74d0825969baa546ee9b9b141b1e446a847,10.1016/j.jtrangeo.2020.102908,Equity in job accessibility and environmental quality in a segmented housing market: The case of Greater London,2021.0,,Li-Ke Jiang; A. Hagen‐Zanker; Prashant Kumar; J. Pritchard,"Public services and resources such as job accessibility and environmental quality are often not equally distributed in spatial and/or social dimensions within metropolitan regions. Availabilities of these public services and resources affect the quality of residential locations, and can be expected to be capitalised into house prices. For prospective house owners, selections of properties will be based on their preferences and appreciations of different attributes of the properties, but also be constrained by their housing budgets. Such a constraint may lead to segmentation of local housing markets into submarkets by price bands, and availabilities of public services and resources, as well as prices of them, might be different between the resulted submarkets. In such a case, distribution of public services and resources might be highly dependent on the public’s abilities to pay for housing, rather than based on their needs for, consumptions of, or expenses on each specific service or resource. Using Greater London as the case metropolitan region, this study examined the existence of housing submarkets by price bands, and explored the differences in availabilities, prices and trade-",13.0,https://eprints.whiterose.ac.uk/168139/1/Jiang%20et%20al%202021%20-%20Equity%20in%20job%20accessibility%20and%20environmental%20quality%20London.pdf
87dab94f36ecf2a8aa93a31ba671bcbc7f68886f,10.1016/j.geoforum.2018.09.009,"The capitalisation of school choice into property prices: A case study of grammar and all ability state schools in Buckinghamshire, UK",2018.0,Geoforum,S. Orford,"Abstract There has been a growing academic and policy debate in the UK on the relationship between school choice, educational performance and house prices. School choice and the chances of attending a good school are important as it relates strongly to educational attainment and qualifications, University entry and access to the labour market. This debate was reinvigorated recently when the Conservative Party announced that state schools which select using academic ability (grammar schools) may be able to expand in England for the first time in decades. Some commentators argued that this may exacerbate and re-enforce existing inequalities in the education system by allowing wealthy parents to ‘buy’ into a particular grammar school via the housing market, leading to “selection by mortgage” as well as by academic ability. This research investigates the extent to which state schools are capitalised into house prices using Buckinghamshire in England as a case study. It differentiates between grammar schools and all ability state schools, using a novel multi-level specification of the repeat sales model. It concludes that single sex boys’ grammar schools attract a higher premium than si",13.0,https://orca.cardiff.ac.uk/114963/1/S%20Orford%202018%20the%20capitalisation%20of%20school%20choice%20postprint.pdf
29512a67674372dca43f5737275731d04f4ed839,10.1016/j.landurbplan.2023.104997,Post-wildfire neighborhood change: evidence from the 2018 Camp Fire,2024.0,Landscape and Urban Planning,Kathryn McConnell; C. Braneon,"As the number of highly destructive wildfires grows, it is increasingly important to understand the long-term changes that occur to fire-affected places. Integrating approaches from social and biophysical science, we document two forms of neighborhood change following the 2018 Camp Fire in the United States, examining the more than 17,000 residential structures within the burn footprint. We found that mobile or motor homes, lower-value residences, and absentee owner residences had a significantly higher probability of being destroyed, providing evidence that housing stock filtering facilitated socially stratified patterns of physical damage. While the relationship between building value and destruction probability could be explained by measures of building density and distance to nearby roads, building type remained an independent predictor of structure loss that we could not fully explain by adding environmental covariates to our models. Using a geospatial machine learning technique, we then identified buildings that had been reconstructed within the burn footprint 20 months after the fire. We found that reconstructed buildings were more likely to have been owner-occupied prior to",13.0,https://doi.org/10.1016/j.landurbplan.2023.104997
6aef4e193b4a83f0f64e996fe22595cb9634ee93,10.3390/su141912427,"Vacancy Dwellings Spatial Distribution—The Determinants and Policy Implications in the City of Sapporo, Japan",2022.0,Sustainability,H. Van; Ha Vinh Tran; T. Asada; M. Arimura,"As the population is shrinking in many municipalities in Japan, one of its effects is the vacant house crisis. The rise of empty houses profoundly affects the city’s society and economy, e.g., property value reduction, increased crime rate, poor sanitation, and housing market stagnation. To better understand the mechanism of the vacant house crisis, the present study proposes to examine the determinants of housing vacancy spatial distribution with the case study of the city of Sapporo. The results highlight the severe vacant cluster in the central city, which would seem to link to the disequilibrium housing market rather than the urban decline. Regarding vacancy determinants, demographic features were the most influential factors, followed by housing and neighborhood characteristics. Specifically, the vacancy correlated strongly with a high density of single households, children, the elderly (in the center), and a high share of offices. The surplus in housing supply and the inelasticity in housing structures also affected the vacancy significantly. On the contrary, a high percentage of private property, household ownership, and the elderly (in suburban) would reduce the vacancy. Fo",13.0,https://www.mdpi.com/2071-1050/14/19/12427/pdf?version=1664512802
2f2eb6b6e0a76e62bcef4348da19926eb625981b,10.21638/spbu07.2020.105,Пространственная структура жилищного строительства в Московской агломерации: радиально-секторальная дифференциация,2020.0,,N. Kurichev; E. Kuricheva; KB Strelka,"The task of this work is to reveal the forms of spatial transformation of the Moscow metropolitan area due to the sectoral and radial differentiation of housing construction. The research is based on the monitoring of all multi-story housing projects under construction and development. These projects are classified by size class, location relative to existing housing stock, housing prices, and distance from highways. The localization of housing projects relative to the main highways changes non-linearly as the distance from the Moscow Ring Road increases. The core of the Moscow metropolitan area is relatively evenly sprawling in the 10-km zone outside the Moscow Ring Road. The nearest suburban zone of Moscow Oblast is being developed not only along key highways, but a significant part of housing projects is implemented in inter-highway intervals. As the distance from the Moscow Ring Road exceeds 10 km, the urban sprawl is replaced by the strengthening of the axes of the settlement pattern along main transport corridors (highways and railways), and the distance of projects from the highways decreases. This trend is particularly evident in the northeastern, eastern and southeastern s",13.0,https://escjournal.spbu.ru/article/download/4632/5529
f8713e591c7667560c2cd38cdec3be9c97dbf77b,10.1108/pm-09-2019-0053,Neural networks support vector machine for mass appraisal of properties,2020.0,,Joseph Awoamim Yacim; D. Boshoff,"The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.,The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.,The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.,The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different propert",13.0,https://repository.up.ac.za/bitstream/2263/81959/1/Yacim_Neural_2020.pdf
c7cae935784ddaddf146b26cb06f8c5f41cd0b3e,10.18488/journal.aefr/2016.6.5/102.5.247.260,Determinants of Housing Prices in an Oil Based Economy,2016.0,,Humoud Almutairi; M. El-Sakka,"This paper is trying to analyze the determinants of housing prices in an oil-based economy, where the price of oil plays a major role in such economies. It is also common to find that government spending represents the most important component of aggregate spending and that governments usually play a central role in the provision of public services to citizens at substantial subsidies, housing is on top of them. The paper is trying to identify the role played by oil price in the housing market in Kuwait. The model is composed of four major determinants of house prices including the price of oil, government expenditures, inflation rate and interest rate. Results confirm the role played by the four factors in determining the price of houses. Variance decomposition indicates that up to 10 quarters, 94.3% of the forecast error variance in housing prices is explained by house price itself, whereas, only 2.3%, 1.6%, 1.5% and 0.8% are explained by Interest rates, inflation rates, government expenditures, and price of oil respectively. The oil price does not seem to play an important impact on price changes in Kuwait. One important recommendation is for the government to relax its monopoly",13.0,https://doi.org/10.18488/journal.aefr/2016.6.5/102.5.247.260
4826b5b6ca475f3223a33b6d9e6bcbe0e8a94cf8,10.28991/cej-2025-011-05-022,eXplainable Machine Learning for Real Estate: XGBoost and Shapley Values in Price Prediction,2025.0,Civil Engineering Journal,Tris Kee; W. K. Ho,"This study examines the application of eXplainable Artificial Intelligence (XAI) in property market research, utilizing housing transaction data from Quarry Bay, Hong Kong. The research employs the XGBoost algorithm to predict property prices and subsequently computes Shapley Additive Explanations (SHAP) values to quantify feature importance. A beeswarm plot is used to visualize the distribution of SHAP values, uncovering complex relationships between prices and property characteristics. The findings demonstrate how features such as square footage and property age contribute to average price predictions, offering valuable insights for urban planning and real estate decision-making. In contrast to the traditional black-box models, this study integrates XAI methodologies to enhance model interpretability, thereby fostering trust in AI-driven market analyses. The novelty of this research lies in its combination of machine learning and explainable techniques, bridging the gap between predictive accuracy and interpretability in property valuation. By advancing data-driven decision-making, this study underscores the potential of XAI in promoting transparency and facilitating informed pol",13.0,https://civilejournal.org/index.php/cej/article/download/5811/pdf
3219663a9c40fb1e9fa07fcdd259854695886200,10.18653/v1/d18-1392,Residualized Factor Adaptation for Community Social Media Prediction Tasks,2018.0,Conference on Empirical Methods in Natural Language Processing,Mohammadzaman Zamani; H. A. Schwartz; Veronica E. Lynn; Salvatore Giorgi; Niranjan Balasubramanian,"Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is relatively older, will use words the same way as those from San Francisco, where the median age is younger with a higher rate of college education. In this paper, we present residualized factor adaptation, a novel approach to community prediction tasks which both (a) effectively integrates community attributes, as well as (b) adapts linguistic features to community attributes (factors). We use eleven demographic and socioeconomic attributes, and evaluate our approach over five different community-level predictive tasks, spanning health (heart disease mortality, percent fair/poor health), psychology (life satisfaction), and economics (percent housing price increase, foreclosure rate). Our evaluation shows that residualized factor adaptation significantly improves 4 out of 5 community-level outcome predictions over prior state-of-the-art for",13.0,https://www.aclweb.org/anthology/D18-1392.pdf
cfc27a33426e6615c17a451873b0b54c65612252,10.1007/s10901-023-10109-y,Multilevel effects of urban form and urban functional zones on housing prices: evidence from open-source big data,2024.0,Journal of Housing and the Built Environment,Guan Huang; Si Qiao; A. Yeh,"While the relationship between local housing prices and the urban form and distribution of urban functional zones in a single city is well-discussed, the conclusion is usually sensitive to a particular city context, and cross-city comparative study is limited. This study attempts to examine the influences of urban form and urban functional zone distribution on housing prices within and between cities after controlling the city-wide socio-economic and demographic differences. Based on multiple open-source big data, such as points-of-interest (POI) and historical housing transaction data, the hierarchical linear model is utilized to compare the housing market of 10 extra-large cities in China. Results indicate that the urban form and the urban functional zone distribution significantly influence housing prices after the socio-economic and demographic differences are controlled. For inter-city comparison, an urban form with high compactness, low centrality, low polycentricity, high density, and low dissimilarity in housing development is related to lower city-level housing prices. For intra-city, proximity to work centers, high-quality hospitals, and schools shows positive association",13.0,https://link.springer.com/content/pdf/10.1007/s10901-023-10109-y.pdf
6afe2c8c44ded3df3660325e2f2d290027bcc140,10.1108/jerer-08-2018-0036,Spatial analysis of Twitter sentiment and district-level housing prices,2019.0,Journal of European Real Estate Research,Christopher M. Hannum; K. Arslanlı; Ali Furkan Kalay,"
Purpose
Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices.


Design/methodology/approach
The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district.


Findings
The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation.


Research limitations/implications
The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a p",13.0,
44a87d523306e4f5fc9f3cb023781fee5e10a220,10.1109/icadeis58666.2023.10270916,"The Comparison Study of Regression Models (Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression) for House Price Prediction in West Nusa Tenggara",2023.0,"International Conference Advancement Data Science, E-learning and Information Systems",Mindi Richia Putri; I. G. P. S. Wijaya; Fritzie Primananda Adi Praja; Abdul Hadi; Faqih Hamami,"Predicting house prices is essential in the real estate industry as it enables stakeholders to make informed decisions. Accurately predicting house prices is paramount in facilitating buying and selling transactions, aiding property valuation, and providing valuable insights for investors and homeowners. This research compares five regression models, namely Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression, to predict house prices in West Nusa Tenggara Province. These considered building area, land area, number of bedrooms, and bathrooms. The data was taken from the public website, namely Lamudi Website, and was collected using a web scraping method and then processed using Machine Learning. By considering factors such as building area, land area, number of bedrooms, and bathrooms, the study aimed to determine which model delivered the most accurate predictions and exhibited a low error rate. This research used various metrics such as R-Squared, Root Mean Square Error (RMSE), and Cross-Validation to measure the accuracy of these models. In this study, the R-Squared and RMSE methods showed that Multiple Linear Regression and Lasso Regression were the",13.0,
84aedeb2329b388be1bf74253e3c2cd2586be562,10.3390/jrfm16010046,The Impact of the 2008 Financial Crisis on Lisbon’s Housing Prices,2023.0,Journal of Risk and Financial Management,João Fragoso Januário; C. Cruz,"Real estate markets are frequently affected by growth and contraction cycles. Given the social and economic impacts of changes on real estate prices, the understanding of these cycles is crucial from a socio-economic perspective, but also, and more importantly, from a public policy view. The literature has provided several contributions focusing on the deconstruction of the main determinants of housing prices. This research focuses on the analysis of housing prices variation with a particular emphasis on the analysis of the impacts of the 2008 financial crisis. Within the existing body of knowledge, few studies have focused on this particular issue, and even fewer have focused on countries where the financial crisis led to an external bailout, as was the case in Portugal. The analysis confirmed that the 2008 financial crisis had a negative impact on real estate prices, and the ex-post growth in GDP and low interest rates had a positive impact. The paper also provides a long-term analysis of housing price trends over the last decades.",13.0,https://www.mdpi.com/1911-8074/16/1/46/pdf?version=1674051376
75ba68147bf6e34f9b631b70a8cea636eda11e65,10.22146/ijg.27036,Use of Geographically Weighted Regression (GWR) Method to Estimate the Effects of Location Attributes on the Residential Property Values,2017.0,,M. Dziauddin; Z. Idris,"This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR) enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property’s physical (structural) attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters.",13.0,https://jurnal.ugm.ac.id/ijg/article/download/27036/pdf
5903681fdc4748e2363cd9eccfe4d7419594224c,10.2139/ssrn.3747255,The Impact of Local Taxes and Public Services on Property Values,2019.0,Social Science Research Network,Anna Grodecka; Isaiah Hull,"Attempts to measure the capitalization of local taxes into property prices, starting with Oates (1969), have suffered from a lack of local public service controls. We revisit this vast literature with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. To make use of the high dimensional vector of controls, as well as time and geographic fixed effects, we employ a novel empirical approach that modifies the recently-introduced debiased machine learning estimator by coupling it with a deep-wide neural network. We find that existing estimates of tax capitalization in the literature, including quasi-experimental work, may understate the impact of taxes on house prices by as much as 50%. We also exploit the unique features of our dataset to test core assumptions of the Tiebout hypothesis and to estimate the impact of public services, education, and crime on house prices. (Less)",13.0,https://doi.org/10.2139/ssrn.3747255
c13ba7ec24a77ebbcb41babca2a687b501814eab,10.1016/j.landurbplan.2025.105389,Efficient and accurate assessment of window view distance using City Information Models and 3D Computer Vision,2025.0,Landscape and Urban Planning,Maosu Li; Fan Xue; Anthony G. O. Yeh,"A distant view through a window is preferred by urban dwellers due to its benefits to human health and well-being. A high window view distance is also valued in real estate markets, especially in high-rise, high-density urban areas. Thus, an urban-scale assessment of window view distance is significant in examining the disparity of sharing of view openness for applications and analytics in urban health, planning and design, and housing. However, current limited assessment methods are neither accurate nor efficient. The evolving photorealistic City Information Models (CIMs) and 3D Computer Vision 10 (CV) enable a new solution due to the high-resolution and efficient semantic representation of urban landscapes. This study aims to present a window view distance index (WVDI) together with an accurate and efficient assessment method using up-to-date 3D CIMs and CV. First, we define the WVDI on a CIM-generated window view image considering the visual permeability of greenery. Then, an automatic assessment of WVDIs is designed on a type-depth window view using 3D semantic segmentation and OpenGL rendering. Experimental tests in Hong Kong Island and Kowloon Peninsula of Hong Kong confirmed",13.0,
c32c258bc409a56d3841a46a421cecf33cd66d99,10.26599/bdma.2019.9020023,Text-based price recommendation system for online rental houses,2020.0,Big Data Mining and Analytics,Lujia Shen; Qianjun Liu; Gong Chen; Shou-Ling Ji,"Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. Using two chronologically extracted datasets of the same four cities, we revealed important factors (e.g., indoor equipment and high-density area) that positively or negatively affect each property's price, and evaluated our preliminary and enhanced models. Our models achieved a Root-Mean-Square Error (RMSE) of 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles, and 26.31 in New York City, which are comparable to an existing model that uses more features.",13.0,https://ieeexplore.ieee.org/ielx7/8254253/9007868/09007875.pdf
9fae76e9d08fe817b5237b9361cc88437096bdc9,10.3390/ijfs11030106,A Systematic Bibliometric Analysis of the Real Estate Bubble Phenomenon: A Comprehensive Review of the Literature from 2007 to 2022,2023.0,International Journal of Financial Studies,José-Francisco Vergara-Perucich,"This article presents the results of a bibliometric review of the study of real estate bubbles in the scientific literature indexed in Web of Science and Scopus, from 2007 to 2022. The analysis was developed using a sample of 2276 documents, which were reviewed in R software and analyzed with the assistance of the Bibliometrix package of the same software. The results indicate that there has been considerable productivity on the topic of real estate bubbles since 2007, with an emphasis on housing price formation processes and the social effects when bubbles burst. The authors found that there were not many case studies located in Latin America or Africa, nor were there approaches with advanced predictive modeling techniques using machine learning or artificial intelligence. The article provides an understanding of the state of the art in real estate bubble research and situates new research in front of the influential literature previously published.",13.0,https://www.mdpi.com/2227-7072/11/3/106/pdf?version=1692764130
670518142f78514470ad2ea85cc37f0ccb4483aa,10.1007/s11116-022-10295-8,"Low-emission zones, modes of transport and house prices: evidence from Berlin’s commuter belt",2022.0,Transportation,E. Aydin; Kathleen Kürschner Rauck,"We study the impact of a tightening of a private driving restriction in Germany’s capital, the city of Berlin, on house prices in its affluent suburbs. Using geo-referenced data on train stations, motorway access points and offers of single-family houses for sale from Germany’s leading online property broker ImmobilienScout24 in a spatially staggered DiD framework, we find evidence for sizeable price growth premia for houses located in walking distance of train stations that lie within 30 min commuting duration to Berlin main station. Property located in immediate vicinity (5 min walking distance) of train stations within 30–40 min commuting duration, however, face penalties. Our findings are of relevance for the design of public infrastructure planning policies that seek to accommodate and facilitate changes in local demands for alternative and more environmentally sustainable modes of transport induced by private driving restrictions.",13.0,https://link.springer.com/content/pdf/10.1007/s11116-022-10295-8.pdf
cf644bbdd9bd19b22aabd0f2ca281158506607e0,10.13043/dys.75.5,Testing for Bubbles in the Colombian Housing Market: A New Approach,2015.0,,J. Gómez-González; Jair N. Ojeda-Joya; Catalina Rey-Guerra; Natalia Sicard,"In the context of financial crises influenced by the development and burst of housing price bubbles, the detection of exuberant behaviors in the financial market and the implementation of early warning diagnosis tests are of vital importance. This paper applies the bubble-detection methodology developed by Phillips, Shi and Yu (2012) to the most important Colombian residential property market. The empirical results suggest that this housing market experienced a price bubble that began in the second half of 2012. This result holds true under alternative robustness checks, namely alternative price deflators, regression windows and price segments. The only exception is in the case of the low-price segment of the housing market where prices have not increased recently. Bubble episodes, under this approach, consist of periods of explosive behavior of the nominal asset price which are not explained by the evolution of its market returns.",13.0,https://doi.org/10.13043/dys.75.5
55736d315d7ddbddf68a2b9c919fa123ed40b87a,10.1016/j.jtrangeo.2021.103029,Geographically weighted regression models of residential property transactions: Walkability and value uplift,2021.0,Journal of Transport Geography,Edmund J. Zolnik,"Abstract Value uplift refers to the increase in property value attributable to proximate transportation infrastructure. The empirical literature is generally supportive of the argument to capture a fraction of such additional value to operate public transportation systems. Unfortunately, attention to the relative value of different transportation infrastructure besides rail infrastructure is not evident in the literature. The gap in the literature is problematic because an accurate estimate of the total revenue potential from present transportation infrastructure investments is not realistic nor is an accurate estimate of the value uplift potential from future transportation infrastructure investments realistic. In order to fill the gap, adoption of a geographically-weighted approach expands the temporal scale of analysis to more than a decade and the spatial scale of analysis to an entire heavy rail system in order to properly contextualize the value uplift from pedestrian infrastructure and from rail infrastructure. Results advance the state of knowledge on value uplift to show the range of value uplift attributable to walkability in proximity to heavy rail stations. Overall, the",13.0,
2b800893d0f199087beb77ece2e1ae1f6a447d34,10.1007/s10037-023-00187-4,COVID-19 and housing prices: evidence from U.S. county-level data,2020.0,Review of Regional Research,Hakan Yilmazkuday,"This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The effects of COVID-19 cases on housing prices are formally investigated by using a two-way fixed effects panel regression, where county-specific factors, time-specific factors, and mobility measures of individuals are controlled for. The benchmark results show evidence for negative and significant effects of COVID-19 cases on housing prices, robust to the consideration of several permutation tests, where the negative effects are more evident in counties with higher poverty rates. Exclusion tests further suggest that U.S. counties in the state of California or the month of May 2020 are more responsible for the empirical results, although the results based on other counties and months are still in line with the benchmark results.",13.0,https://link.springer.com/content/pdf/10.1007/s10037-023-00187-4.pdf
60165a1034e15dbe03f24a89b680854c23978e94,10.1051/e3sconf/202341803001,A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms,2023.0,E3S Web of Conferences,Lale El Mouna; H. Silkan; Youssef Haynf; Mohamedade Farouk Nann; Stéphane C. K. Tekouabou,"Accurate housing price forecasts are essential for several reasons. First, it allows individuals to make informed decisions about buying or selling real estate and to determine appropriate prices. Secondly, it helps real estate agents and investors make better investment decisions and negotiate contracts more effectively. In addition, housing prices are often an indication of the general state of the economy. A price decrease may indicate an economic recession, while an increase in prices may signal economic growth. In this study, we proposed to address this subject by predicting house prices using machine learning by choosing three types of machine learning: Linear Regression (LN), Random Forest (RF) and GradientBoosting (GB). We tested our models on the Melbourne real estate dataset, which includes 34,857 property sales and 21 features.",13.0,https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/55/e3sconf_acc2023_03001.pdf
7a34439f9b2bb695784c456c03a4a8967ff3640e,10.1111/eufm.12325,Leading indicators for US house prices: New Evidence and Implications for EU Financial Risk Managers,2021.0,European Financial Management,Miguel Rodriguez Gonzalez; Tobias Basse; Danilo Saft; Frederik Kunze,"Correspondence Miguel Rodriguez Gonzalez, Centre for Risk and Insurance, Leibniz University Hannover, Otto‐Brenner‐Straße 7, 30159 Hannover, Germany. Email: mr@ivbl.uni-hannover.de Abstract This study draws on machine learning as a means to causal inference for econometric investigation. We utilize the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case‐ Shiller 20 City Composite Home Price Index (SPCS20). The empirical evidence implies that the survey data can help to predict US house prices. This finding extends the results of Granger causality tests performed by Rodriguez Gonzalez et al. in 2018 using a new machine learning approach that methodologically differs from traditional methods in empirical financial research.",13.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/eufm.12325
95c91ff86c983b86215843107954c3c67dabf3c5,10.3846/1648715x.2011.585185,The impact of pricing strategies on time-on-market under various economic conditions,2012.0,,E. Hui; Ka Yu,"This study examines how price adjustments in a flat's marketing phase, along with various housing attributes, influence its time-on-market (TOM). A Cox survival model is used for the analysis of two separate periods, one of economic downturns (2003) and one of economic recovery (2004--2006), in Hong Kong. Factors such as price adjustments, sale price, numerous housing attributes, general property price trend, and changes in unemployment rate have significant impacts on TOM, with their respective impacts changing over time. Specifically, the effectiveness of raising list prices before transactions, in optimizing sellers’ returns and TOM, depends on economic conditions as well as on the market expectations regarding future property prices. Directions on future studies are then discussed.",13.0,https://journals.vgtu.lt/index.php/IJSPM/article/download/4833/4146
9a61826a4d6289d3bd9817f0024f1f7e5da3eb05,10.5130/ajceb.v2i2.2901,Building a House Prices Forecasting Model in Hong Kong,2012.0,,Xin Janet; K. Lam,"This paper builds a house prices forecasting model for private residential houses in HongKong, based on general macroeconomic indicators, housing related data and demographicfactors for the period of 1980 to 2001. A reduce form economic model has been derivedfrom a multiple regression analysis where three sets and eight models were derived foranalysis and comparison. It is found that household income, land supply, population andmovements in the Hang Seng Index play an important role in explaining house pricemovements in Hong Kong. In addition, political events, as identified, cannot be ignored.However, the results of the models are unstable. It is suggested that the OLS may nota best method for house prices model in Hong Kong situation. Alternative methods aresuggested.",13.0,https://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/download/2901/3078
57589715ac838c44bd65d5726a17d94530ed29c6,10.3390/land10101009,House Price Forecasting from Investment Perspectives,2021.0,Land,S. Shi; V. Mangioni; X. Ge; S. Herath; F. Rabhi; Rachida Ouysse,"Housing market dynamics have primarily shifted from consumption- to investment-driven in many countries, including Australia. Building on investment theory, we investigated market dynamics by placing investment demand at the center using the error correction model (ECM). We found that house prices, rents, and interest rates are cointegrated in the long run under the present value investment framework. Other economic factors such as population growth, unemployment, migration, construction activities, and bank lending were also important determinants of the housing market dynamics. Our forecasting results show that the Sydney housing market will continue to grow with no significant price decline in the foreseeable future.",13.0,https://www.mdpi.com/2073-445X/10/10/1009/pdf?version=1632803822
cb5a7b1e772084f00688ab23f5c51c41012553e1,10.1257/app.20220329,The Simpler the Better? Threshold Effects of Energy Labels on Property Prices and Energy Efficiency Investments,2025.0,American Economic Journal: Applied Economics,Rodolfo Sejas-Portillo; Mirko Moro; Till Stowasser,"Using British data on over 7 million residential property sales, we find that the simplified energy efficiency (EE) information leads to important market effects. UK legislation requires the use of an energy label that shows an EE rating score (1–100) mapped into arbitrary color-coded rating bands (A–G). Applying a regression discontinuity design, we document significant price discontinuities at rating band thresholds, indicating heuristic thinking in the market. These price discontinuities influence market behavior as sellers are more likely to make EE investments if their property is just below a threshold. We discuss policy considerations and avenues for future research. (JEL D91, K32, Q48, R31)",13.0,http://dspace.stir.ac.uk/bitstream/1893/36477/2/The_Simpler_The_Better_%20Online_Appendix.pdf
2ce98014780d8a025c9acf3e943a33fd71e8d5b6,10.1016/j.jfe.2017.02.002,Forest pests and home values: The importance of accuracy in damage assessment and geocoding of properties,2017.0,,K. Moeltner; C. Blinn; T. Holmes,We examine the impact of measurement errors in geocoding of property locations and in the assessment of Mountain Pine Beetle-induced tree damage within the proximity of a given residence on estimated losses in home values. For our sample of homes in the wildland-urban interface of the Colorado front range and using a novel matching estimator with Bayesian regression adjustment we find that both types of errors can lead to substantial biases in estimated losses. Our results confirm that the Forest Service's Aerial Detection Survey is generally too coarse to be informative for property valuation that depends on highly localized spatial data.,13.0,https://vtechworks.lib.vt.edu/bitstream/10919/97634/1/moeltner2017.pdf
7fc0065794762b847c9bb68776346c0d1b3f4aa7,10.1080/21649480.2013.870490,"Monetary policy, mortgage rates and the housing bubble",2013.0,,J. F. Mcdonald; H. Stokes,The article uses three alternative models and monthly data to investigate whether the Federal Funds Rate or the rate on standard 30 year mortgages in the US for the period 1987 to 2010 impacts an index of housing prices. The results indicate that positive shocks to the Federal Funds Rate are associated with housing price changes in the negative direction and the mortgage rate in the positive direction. Shocks to the mortgage rate have no statistically significant impact on housing prices except when the data are filtered with the Christiano and Fitzgerald (2003) procedure and a vector auto-regression model using 16 lags is estimated.,13.0,https://www.econstor.eu/bitstream/10419/147694/1/21649480.2013.870490.pdf
89150a647d9e1a08eb1878266bd50b940d68ac55,10.1111/cico.12436,Black Homebuying after the Crisis: Appreciation Patterns in Fifteen Large Metropolitan Areas,2019.0,City & Community,Dan Immergluck; Stephanie Earl; Allison Powell,"Some have questioned the financial wisdom of homeownership and, especially, Black homeownership. This is understandable because the mortgage crisis dealt heavy blows to Black homeowners. One concern is that home values may not appreciate as much where Blacks purchase homes. We examine how Black homebuyers fared compared to White and Latino buyers in terms of home appreciation during the 2012 to 2017 recovery. We examine appreciation rates by race and ethnicity across 15 metros. We then estimate the relationship between appreciation and the race and ethnicity of the homebuyer, as well as characteristics of the neighborhood where the home is purchased. We find Blacks saw higher appreciation rates than Whites in high– and medium–appreciation metros, but not in low–appreciation metros. We also find that in medium– and high–appreciation metros, buyers in racially diverse neighborhoods tended to see higher levels of appreciation. Also in higher–appreciation metros, those buying in lower–income neighborhoods tended to see higher appreciation rates, while those in low–appreciation metros did not.",13.0,
acb2aa00a1957b93d67befab7ff24d3bce05c99a,10.1080/09599916.2021.1890187,Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data,2021.0,Journal of Property Research,Prodosh E. Simlai,"ABSTRACT In this paper, we introduce machine-learning (ML) methods to evaluate one of the key concepts of real estate analysis – the prediction of housing prices in the presence of a large number of covariates. We use several supervised ML tools that are based on regularisation methods – notably Ridge, LASSO, and Elastic Net regressions – and discuss their relative performance in comparison to conventional OLS-based methods. Our empirical results show that the supervised ML methods provide a comprehensive description of the determinants of owner-occupied housing values in the census tracts of California. We find that, compared to the familiar worlds of OLS and WLS, the Ridge, LASSO, and Elastic Net regressions provide relatively better out-of-sample predictions. Among the benefits of shrinkage-based ML methods are their ability to resolve such issues as variable selection and overfitting.",13.0,
abdcace94f2704496eeea64c035cd4e01ff45963,10.1109/mlbdbi54094.2021.00059,Decision Trees for Objective House Price Prediction,2021.0,"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)",Zhishuo Zhang,"Different people buy houses with the same value at different prices, which usually leads to dissatisfaction with housing prices and unfair housing prices. To solve this problem, we designed an objective housing price prediction scheme based on a decision tree. First, we selected 5 important features based on the decision tree for subsequent modeling. Then we designed a housing price prediction model based on a decision tree. To obtain the optimal parameters, we used grid search. The results showed that the number of houses is the most important factor affecting housing prices, followed by the local population's quality, geographic location, education, and crime rate. To verify the effectiveness of the decision tree scheme, we compared it with some other advanced machine learning models. The implementation results show that our scheme achieves the best results.",13.0,
86a451f6ea9c7f7eda2c0cd82db2903990ccb1f3,10.1111/jors.12425,Do land use restrictions increase restaurant quality and diversity?,2019.0,Journal of Regional Science,D. Shoag; Stan Veuger,"There is significant evidence that restrictions on residential land use reduce housing supply, increase house prices, and limit inflows of low‐income households. Local decision‐makers often argue that their efforts are mere attempts to preserve local amenities. We provide evidence that there is some truth to this claim: that residents of cities with more restrictions on land use appear to have access to higher‐quality and more diverse restaurants. In the process, we develop measures of restaurant quality based on organically generated data that, while strongly correlated with expert assessments, are more easily calculated at high frequencies and levels of geographic granularity.",13.0,
da958e8219ddb9cfcf129b7ab1fb26697625b958,10.2139/ssrn.840184,"Airport-Related Noise, Proximity, and Housing Prices in Atlanta",2006.0,,Jeffrey P. Cohen; Cletus C. Coughlin,,13.0,https://doi.org/10.2139/ssrn.840184
5a3d0caa40a6f1c0c02b9c2b731c3caceb87d1e2,10.1016/j.apgeog.2024.103479,"Urban terrain, mountain landscape, and housing price: A heterogeneous investigation of the amenity effects in a mountainous city (Guiyang) from the vertical dimension",2025.0,Applied Geography,Xingyin Wang; Haizhen Wen; Bin Gui; Zhenlong Liu; Liu Yang,,13.0,
e1ec1b999c9d283637d0daa85c523651f6de3bec,10.1007/s11356-023-30123-5,The nonlinear relationship between air quality and housing prices by machine learning,2023.0,Environmental science and pollution research international,Wei-Wen Zhang; Sheng Pan; Zhi-Yuan Li; Zi-Qing Li; Zhaoyingzi Dong,,13.0,
0b0a26f8891fa7516a6b0a7e234002eed48217d4,10.1007/s12652-019-01616-4,The usage of artificial intelligence in the commodity house price evaluation model,2020.0,Journal of Ambient Intelligence and Humanized Computing,Xiang Zhou,,13.0,
8668b7e56b1ea1030296fd1fcdeddbc1e7a91421,10.1016/0094-1190(81)90028-0,Ridge estimation of house value determinants,1981.0,,John E. Anderson,,13.0,
4d86f0e95e1fafc3fcd2a4b2edb570bb29079415,10.21273/hortsci.50.3.402,Carbon Footprint and Production Costs Associated with Varying the Intensity of Production Practices During Field-grown Shrub Production,2015.0,,C. Hall; D. Ingram,"This research uses a life cycle analysis and economic engineering approach to determine the costs and global warming potential (GWP) of production and postproduction practices associated with Taxus 3media ‘Densiformis’, which is often grown using a more capital-intensive regime during the propagative and harvesting stages than the typical field-grown shrub. Total variable costs incurred during the rooted cutting stage were slightly over $0.24 per marketable rooted cutting. This was made up of $0.1966, $0.032, and $0.0127 for labor, materials, and equipment operating costs, respectively. The GWP of materials and equipment used during the rooted cutting stage of production was 0.0097 and 0.2762 kg CO2 equivalent (CO2e), respectively. Equipment costs in this phase were predominantly from heating the greenhouse (92%) and the greenhouse heating functions comprised 95% of the rooting cutting GWP. GWP during the post-farm gate stage was 2.4506 kg CO2e per marketable shrub but was offset by 12.5522 kg CO2 being sequestered in the shrub during its time in the landscape and weighted over the 100-year assessment period, leaving a net GWP of –8.1824 kg CO2e per marketable shrub by the end of t",12.0,https://journals.ashs.org/downloadpdf/journals/hortsci/50/3/article-p402.pdf
499312306c877bc6841521aa0ecb312519eb12fb,10.18402/resci.2020.04.07,Spatiotemporal characteristics and formation mechanism of rural residential land transition from the perspective of human-land relationship,2020.0,资源科学,Ran Shang; Yan-Bo Qu; Huailong Jiang,": Land use transition (LUT) provides theoretical and practical guidance for the regulation of the conflicts in human-land relationship in the process of social and economic development. As an important part of rural land use, rural residential areas are closely related to the migration and movement of rural population. Systematically understanding the transition of rural human- land relationship and its formation mechanism can provide a theoretical basis for rural transition and development. In view of the reality of the loss of rural population and the hollowing of rural residential areas in the process of urbanization, this study depicted the theoretical process of rural residential area transition (RRAT) from the perspective of the changing human-land relationship. Then, per capita rural residential area (PCRA) was used as the characterization index of RRAT, and spatial autocorrelation least linear quadratic regression model (ordinary least squares) and geographically weighted regression (GWR) models were used to reveal the spatial and temporal characteristics of RRAT and its influencing factors and mechanisms. The empirical analysis of Shandong Province from 1996 to 2016 shows ",12.0,http://www.resci.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=47469
c7ad7491fb8d336fd680ebdc684e9a62a9eae81c,10.1186/s12937-024-01050-z,Greater adherence to the Mediterranean diet pattern in the United States is associated with sustainability trade-offs,2024.0,Nutrix Journal,Zach Conrad; Madison Korol; Chloe DiStaso; Songze Wu,"The Mediterranean diet pattern has been consistently associated with health benefits but less is known about the association with environmental and economic sustainability in the United States (US). This information is needed to support sustainable policy agendas and provide consumers with evidence-based information needed to make informed food choices. This study fills this research gap by evaluating the environmental sustainability and diet cost associated with adherence to the Mediterranean diet pattern in the US. Dietary data from the National Health and Nutrition Examination Survey (2011–2018, n = 17,079) were merged with data on environmental impacts (greenhouse gas emissions, cumulative energy demand, water scarcity footprint), agricultural resource demand (land, fertilizer nutrients, and pesticides), and food prices from multiple publicly available databases. The Mediterranean Diet Score was used to evaluate adherence to the Mediterranean diet pattern. Multivariable linear regression models were used to evaluate the association between adherence to the Mediterranean diet pattern and environmental impacts, agricultural resource demand, and diet cost. Sensitivity analyses wer",12.0,https://doi.org/10.1186/s12937-024-01050-z
a3c68f008eb845572eb67a041f505b0fed83323a,10.1016/j.jhe.2019.03.004,Interest-only-mortgages and housing market fluctuations in Denmark,2019.0,,Peter Karpestam; Sebastian Johansson,"Abstract Low repayment rates on mortgages have probably contributed to increased macroeconomic imbalances for several countries in recent years. The objective of this paper is to analyze how the introduction of interest-only-mortgages in 2003 affected the Danish housing market. Using quarterly data between 2001 and 2013, we analyze the long- and short-run relationships between mortgage repayments and three dependent variables: real house prices; the number of property sales; and mortgage debts in relation to disposable income. We find that including mortgage repayments in nominal housing payments improves the econometric fit when predicting house prices and mortgages in relation to disposable incomes in both the long run and the short run, but not when modeling sales. Also, the conventional user cost variable either turn out insignificant or with the “wrong” (i.e. positive) sign, while nominal housing payment, including mortgage repayments, are significantly negative. However, some regression results are sensitive to specification and there are some indications of reversed causality issues. We therefore perform complementary event studies. The results of the event studies suggest t",12.0,http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-1272
3879c480a0acae6d87b7a228ff348ccbbd853136,10.1007/s10901-021-09898-x,"Assessing economic, social and environmental impacts on housing prices in Hong Kong: a time-series study of 2006, 2011 and 2016",2021.0,Journal of Housing and the Built Environment,Jing Li; Weixuan Fang; Yuan Shi; Chao Ren,"Given Hong Kong’s unique high-density urban environment and limited land resources, more and more general public has been concerned about the living quality. Based on three waves of census data (2006, 2011 and 2016), combined with our spatial–temporal urban environmental database consisting of three local datasets of urban climate and air quality, this paper assesses the impacts of social, economic and environmental factors on the logarithm of housing prices in Hong Kong through linear regression analysis. Specifically, both supply- and demand-side economic factors have significant impacts on housing prices. Demographic factors are not as significant as expected in affecting housing prices. Transportation factors have more significant effects in the short run than in the long run. Environmental factors, including the number of hot night hours, Annual Air Quality Index (AAQI) of nitrogen dioxide (NO2) and particulates with particle sizes less than 10 microns (PM10), significantly affect housing prices over time. The results have important implications: current policy instruments to prevent housing price escalation are focused on increasing property tax or land supply (economic facto",12.0,https://link.springer.com/content/pdf/10.1007/s10901-021-09898-x.pdf
615cab34abada51059e7bf812011aa18b664bad3,10.3390/technologies12080128,Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices,2024.0,Technologies,Areti Karamanou; Petros Brimos; E. Kalampokis; Konstantinos A. Tarabanis,"In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate m",12.0,https://www.mdpi.com/2227-7080/12/8/128/pdf?version=1723008905
c346c7df3038941d44971fc34c6be37a646c642d,10.1016/j.socscimed.2023.116538,Economic activity and suicides: Causal evidence from macroeconomic shocks in England and Wales.,2023.0,Social Science & Medicine (1967),Gabriele M. Lepori; Sara Morgan; B. Assarian; Tapas Mishra,"The relationship between economic activity and suicides has been the subject of much scrutiny, but the focus in the extant literature has been almost exclusively on estimating associations rather than causal effects. In this paper, using data from England and Wales between January 1, 1997 and December 31, 2017, we propose a plausible set of assumptions to estimate the causal impacts of well-known macroeconomic variables on the daily suicide rate. Our identification strategy relies on scheduled macroeconomic announcements and professional economic forecasts. An important advantage of using these variables to model suicide rates is that they can efficiently capture the elements of 'surprise or shock' via the observed difference between how the economy actually performed and how it was expected to perform. Provided that professional forecasts are unbiased and efficient, the estimated 'surprises or shocks' are 'as good as random', and therefore are exogenous. We employ time series regressions and present robust evidence that these exogenous macroeconomic shocks affect the suicide rate. Overall, our results are consistent with economic theory that shocks that reduce estimated permanent ",12.0,https://doi.org/10.1016/j.socscimed.2023.116538
6e9c26a825457de53154364c62dc8ae8b8baf5a1,10.1111/tgis.13303,Spatial Performance Approach to Machine Learning Algorithms: A GIS‐Based Comparison Analysis for Real Estate Valuation,2025.0,Transactions on GIS,Nihal Genç; H. E. Colak; Ferdi Ozbilgin,"Machine learning algorithms are frequently used in mass valuation activities today. These algorithms are believed to perform differently depending on various factors such as different functions, model parameters, data quantity, and quality. Another factor that is often overlooked is location. In this study, the performance of a set of machine learning algorithms in predicting real estate values was evaluated from a spatial perspective. To highlight the spatial performance of the algorithms, a study area was chosen where the factors affecting value are spatially heterogeneous. Value predictions were made using Linear Regression, Random Forest, Support Vector Machine, Regression Trees, Gaussian Process Regression, Artificial Neural Network, and Least‐Squares Boosting algorithms. Bayesian Optimization was used to improve the performance of the algorithms. The performances of the algorithms were compared using R2, RMSE, MSE, MAPE, and MAE metrics. The Random Forest model showed the best performance according to these metrics. Spatial predictions were made in the GIS environment using the IDW spatial interpolation method based on test data. The spatial distribution of the predictions an",12.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/tgis.13303
299d1caeffba2effdb0e828a5bb0729add2502ec,10.3390/su11226203,Elevated Risk of Ecological Land and Underlying Factors Associated with Rapid Urbanization and Overprotected Agriculture in Northeast China,2019.0,Sustainability,Shu-Han Liu; Dong-Yan Wang; Guo-Ping Lei; Hong Li; Wen-Bo Li,"Ecological land with considerable ecological value can be regarded as an important indicator in guaranteeing ecosystem function and sustainable development. Generally, the urbanization process has been considered to be the primary factor affecting ecological land use. However, the influence of agricultural development, particularly in a typical farming area, has rarely been studied. In this paper, we present a method to assess the ecological risk of ecological land (ELER) in a black soil area in northeastern China. Furthermore, the underlying factors were detected using the geographically weighted regression model, which took into account conditions of natural elements, the urbanization process, and grain production conditions. The results indicate that ecological land experienced remarkable changes with an evident loss and decline from 1996–2015. The ELER progressively increased in the concentrated farming area and the western agro-pastoral ecotone, and the ecological land in the eastern forest area was always at a high risk level. According to the regression coefficients, the relationships between influence factors and ELER could be better explained by the variables of elevation,",12.0,https://www.mdpi.com/2071-1050/11/22/6203/pdf?version=1573037374
7883bb23064295a87ed43d17013884328377d12c,10.1007/s11769-023-1360-2,Leave or Stay? Antecedents of High-level Talent Migration in the Pearl River Delta Megalopolis of China: from a Perspective of Regional Differentials in Housing Prices,2023.0,Chinese Geographical Science,Chen Shen; Yang Wang; J. Zuo; R. Rameezdeen,"Rapid urbanization and population growth have triggered an increase in urban housing demand and rising housing prices, which can influence the migration intention of high-level talents. Much work within the literature has focused more on the migration of the general public. However, antecedents of migration intention and the impact of housing prices on the migration of high-level talents remain unclear. Therefore, based on the push-pull theory, this study investigates the influencing factors of talent migration intention and explores the role of housing prices. This study reveals a complex mechanism underlying migration decisions by using logistic regression models and survey data of high-level talents in the Pearl River Delta (PRD) megalopolis of China. The results indicate that: 1) in high house-price regions, social integration is the primary push factor, and the main factors for retaining talents are the expectation of future work and intimate family relationships; 2) in medium house-price regions, the main factors that attract talents are the current salary level and close family ties; 3) in low house-price regions, living convenience is a determining factor in retaining talen",12.0,http://egeoscien.neigae.ac.cn/EN/10.1007/s11769-023-1360-2
79a7694fb1ad440b3a3e8d2a0ec12855dfa0bafc,10.1016/j.sbspro.2016.05.345,Housing Affordability and Income-Threshold in Social Housing Policy,2016.0,,Grazia Napoli; M. Trovato; S. Giuffrida,"Abstract The issue of affordable housing has again become crucial in ensuring a greater social equity, increasing social cohesion and reducing inequalities within metropolitan or regional systems which, in the current climate of severe economic crisis, have to respond to global challenges of development, innovation and sustainability. This study aims at building a system of knowledge about the housing affordability on the territorial scale, by which to identify the local characteristics of housing problems that could be solved through traditional planning tools or new practices of social housing, which involves private stakeholders and/or public administrations. This system of knowledge should include the analysis of the wealth distribution both in terms of income and real estate market price, and the calculation of the income-thresholds (based on the HAI-Housing Affordability Index) that filter the access to the real estate market. All these data are used to define the correspondences between groups of people and affordable housings, distinguishing them both by income and urban location. This approach is applied to the case study of the territorial system of the Province of Syracu",12.0,https://www.sciencedirect.com/science/article/pii/S1877042816304256/pdf
7283885362d039a454c4650ee5244fa5acd31cf0,10.1108/ijhma-09-2021-0103,Discovering the fundamentals of Turkish housing market: a price convergence framework,2022.0,International Journal of Housing Markets and Analysis,İsmail Cem Özgüler; Z. Göknur Büyükkara; C. Küçüközmen,"
Purpose
The purpose of this study is to determine the Turkish housing price and rent dynamics among seven big cities with a unique monthly data set over 2003–2019. The secondary purpose is to examine bubble dynamics within the price convergence framework through alternative tests.


Design/methodology/approach
The paper conducts two autoregressive distributed lag (ARDL) cointegration estimates for housing prices and rents and applies conditional error correction model to investigate the long-run drivers of the Turkish housing market. The authors compare ARDL cointegration in-sample forecasts and discounted cash flow (DCF) estimates with actual prices to determine the timing, magnitude and collapse period(s) of bubbles within the price convergence framework. In particular, the generalized sup augmented Dickey–Fuller (GSADF) approach time stamps multiple explosive price behaviors.


Findings
The ARDL results confirm the theory of investment value by addressing mortgage rates, the price-to-rent ratio and rents as the fundamental factors of house prices. The price-to-rent ratio offers a comparison mechanism among houses deciding to buy a new house in which rents increase monthly real ",12.0,
4ddd46259386d6996e849c404ec8b4879516fd0e,10.1108/jerer-10-2017-0041,Determinants of housing prices from an urban economic point of view: evidence from Hungary,2019.0,Journal of European Real Estate Research,Norbert Czinkán; Áron Horváth,"
Purpose
The purpose of the paper is to investigate a cross section of Hungarian settlement-level unit housing prices with a special emphasis on measuring the effect of population and its growth, along with accessibility to the centre of an aggregated spatial unit such as a micro-region, county or region, for the period of 2001-2011.


Design/methodology/approach
The analysis uses cross-sectional ordinary least squares techniques with Moulton-corrected standard errors. The estimation is guided by the implications of a simplified monocentric urbanized area framework following the model of DiPasquale and Wheaton (1996), and the econometric model is augmented with population growth rate at the settlement level to bridge the theory explaining rents and data base containing prices instead.


Findings
The location is a key factor in determining housing prices: living 10 min further from the centre results in 11 per cent cheaper housing. When estimating bid-rent curves, results show that it is crucial to control for city size and the income effect. The elasticity of housing price with respect to city size is 0.09 according to our preferred model. Population growth has an asymmetric impact",12.0,
b0e87f88f0088dc7094ae48010ca2ff3d341c35e,10.3390/land14020265,"Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China",2025.0,Land,Zhenchao Zhang; Wei-Xin Luan; Chuang Tian; Min Su,"With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. The findings reveal a core–periphery structure, with overall school quality on the rise and basic facility configuration exerting a stronger impact than teacher strength. Among internal resources, per capita sports venue area (PCSFA) and per capita teaching equipment value (PCTRE) contribute most significantly to school quality, while high-quality clusters in traditional educational hubs, university-covered areas, and transitional zones spur improvements in surrounding schools. The population density, housing prices, and the construction land development cycle all positively correlate with school quality, highlighting the need for coordinated action among urban planners, education authorities, and housing regulators to ensure th",12.0,https://www.mdpi.com/2073-445X/14/2/265/pdf?version=1737898420
27bb95221e788786fdbbba7a683c8f71e56fdc41,10.1371/journal.pone.0277776,Impact of built environment on residential online car-hailing trips: Based on MGWR model,2022.0,PLoS ONE,Yanjun Cao; Yong-Zhong Tian; Jinglian Tian; Kangning Liu; Yang Wang,"With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built ",12.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277776&type=printable
a9aa8ff2e63af06927e90407046655fab060c351,10.1007/s11146-022-09916-x,The Size and Spatial Extent of Neighborhood Price Impacts of Infill Development: Scale Matters?,2022.0,Journal of real estate finance and economics,C. Nygaard; G. Galster; Stephen Glackin,"Infill investments are argued to mitigate environmental footprints, regenerate places and accommodating population growth, but frequently generate local opposition. However, there is a dearth of knowledge around how different types of infill affect different segments of local property markets, how persistent effects are and how far they reach. Using detailed geocoded infill development activity and sales data, we test the price level and trajectory impacts of five infill types, distinguished by the net scale of additional dwellings, on property prices within five concentric 100-meter rings. Using an adjusted interrupted time-series estimation strategy with locality, property and neighborhood characteristic controls we find that developments that generate a net increase in dwellings of four or less, typically result in an appreciation in the average sales prices of proximate dwellings. Moderate and large-scale developments generate negative price effects, but these effects predominantly affect apartments and townhouses, not the dominant detached house submarket. Over time, amenity effects and local market potential may even have a further positive expectation effect on detached hous",12.0,https://link.springer.com/content/pdf/10.1007/s11146-022-09916-x.pdf
99e1e6314dcad04e7be39a5595dec7224ddf907c,10.1111/gean.12247,Retail and Place Attractiveness: The Effects of Big‐Box Entry on Property Values,2020.0,Geographical Analysis,Sven-Olov Daunfeldt; Oana Mihaescu; Ö. Öner; N. Rudholm,"Opponents of big-box entry argue that large retail establishments generate noise and other types of pollution and a variety of negative externalities associated with traffic. Big-box advocates, on the other hand, argue that access to a large retail market delivers not only direct economic benefits but also a variety of positive spillover effects and therefore can be considered a consumer amenity that increases the attractiveness of the entry location. To test the validity of these competing arguments, we use the entry of IKEA in Sweden as a quasi-experiment and empirically investigate whether increased access to retail affects place attractiveness, which is proxied by residential property values. We find that IKEA entry increases the prices of the properties sold in the entry cities by, on average, 4.4% or 60,425 SEK (approximately 6,400 USD), but this effect is statistically insignificant for the properties in the immediate vicinity of the new IKEA retail trade area. In addition, we observe an attenuation of the effect with distance from the new IKEA store and the associated retail trade area, where the properties located 10 km away experience a 2% price increase. Our results are ",12.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/gean.12247
39c04ab14dd76efab8e972305536d9ad45ee5f85,10.3390/su12093869,"Examining Spatial Heterogeneity Effects of Landscape and Environment on the Residential Location Choice of the Highly Educated Population in Guangzhou, China",2020.0,Sustainability,Yang Wang; Kang-Min Wu; Jing Qin; Chang-Jian Wang; Hong'ou Zhang,"The residential location choice of the highly educated population is an important consideration to construct a livable city. While landscape and environment are important factors, few studies have deeply analyzed the spatial heterogeneity effects of landscape and environment on the residential location choices of a highly educated population. Taking Guangzhou as the sample, we built a livability-oriented conceptual framework of landscape and environment, and constructed datasets for highly educated population proportion, landscape, and environment factors, and other influencing factors for Guangzhou’s 1364 communities. Global regression and geographically weighted regression (GWR) models are used for analysis. The GWR model is more effective than the global regression model. We found spatial heterogeneity in the strength and direction of the relationship between the highly educated population proportion and landscape and environment. We find that landscape and environment exert spatial heterogeneity effects on the residential location choice of the highly educated population in Guangzhou. The conclusions will be of reference value to further understand how the spatial limitations o",12.0,https://www.mdpi.com/2071-1050/12/9/3869/pdf?version=1589277947
9e1016f9132795f302abd38ed5226e9d1c79fc91,10.1080/13658816.2024.2391412,"A backfitting maximum likelihood estimator for hierarchical and geographically weighted regression modelling, with a case study of house prices in Beijing",2024.0,International Journal of Geographical Information Science,Yi-Gong Hu; Richard Harris; Richard Timmerman; Binbin Lu,"Abstract Geographically weighted regression (GWR) and its extensions are important local modelling techniques for exploring spatial heterogeneity in regression relationships. However, when dealing with spatial data of overlapping samples – for example, when precise locational information is aggregated to a shared neighbourhood to avoid revealing the addresses of individual survey respondents – GWR-based models can encounter several problems, including obtaining reliable bandwidths. Because data with this characteristic exhibit spatial hierarchical structures, we propose combining hierarchical linear modelling (HLM) with GWR to give a hierarchical and geographically weighted regression (HGWR) model that divides coefficients into sample-level fixed effects, group-level fixed effects, sample-level random effects, and group-level spatially weighted effects. This paper presents a back-fitting likelihood estimator to fit the model, a simulation experiment that suggests that HGWR is better able to capture these effects and the spatial heterogeneity within them than are traditional HLM or GWR models, and a case study looking at predictors of housing price in Beijing, China. The ability of ",12.0,https://doi.org/10.1080/13658816.2024.2391412
ee3f8de7c1af011e16f0a2714b5c679d0d5bff1d,10.3390/ma16175875,The Influence of Heat Aging Treatments on the Cavitation Erosion Behavior of a Type 6082 Aluminum Alloy,2023.0,Materials,I. Bordeașu; B. Ghiban; L. Micu; A. Luca; A. M. Demian; Dionisie Istrate,"It is known that a number of parts that operate in liquid media, such as the propellers of motorboats and pleasure river vessels, as well as the rotors of household pumps and the radiators and pumps in the cooling system of motor vehicles are made, as a rule, of aluminum-based alloy. Research during maintenance leads to the conclusion that, in certain operating conditions, due to the turbulent character of the flow, with pressure drops to below the vaporization level, it inevitably reaches the threshold of cavitation, which manifests itself through its effects, especially through erosion. To increase the lifetime, these alloys are currently subjected to techniques to improve the structure’s resistance to the cyclic stresses of cavitational microjets. Among these techniques are volumetric heat treatments, which lead to changes in the microstructure and mechanical property values, with an effect on the behavior and resistance to cavitation erosion. This paper studies the influence of heat aging treatments on the cavitation erosion behavior of an aluminum alloy type 6082, in the cast state. The heat treatments applied were 140 °C/1 h, 12 h, 24 h and 180 °C/1 h, 12 h, 24 h. The MDEmax ",12.0,https://www.mdpi.com/1996-1944/16/17/5875/pdf?version=1693208745
7427027199417a18a70be69cb0f3a0e0b8d51fb0,10.24136/eq.2020.001,Macroeconomic imbalance procedure (MIP) scoreboard indicators and their predictive strength of “multidimensional crises”,2020.0,,Krzysztof Biegun; J. Karwowski,"Research background: The evaluation of the predictive strength of MIP indicators in relation to crises is extremely important for the process of coordinating the economic policies of the EU countries. MIP is one of the pillars of the economic crisis prevention procedure. Predictive power of individual indicators has not been tested before their introduction. Purpose of the article: Evaluation of the predictive strength of fourteen MIP indicators in relation to multidimensional crises in the EU countries. Methods: We used ordered probit model to test the ability of MIP indicators to correctly predict episodes of “multidimensional crises” (as defined by the authors) in the period between 2008 and 2017 in all EU Member States. Findings & Value added: We defined “multidimensional crises”, combining several negative phenomena into one limited dependent variable. This work is also novel in its application of probit regression to test the predictive strength of MIP indicators with an ordered probit model. We identified five MIP variables which were statistically significant in predicting “multidimensional crises” for all EU countries: net international investment position, nominal unit la",12.0,http://economic-research.pl/Journals/index.php/eq/article/download/1761/1651
debb879b08a61a69a9774762e42c5e90adead863,10.1002/ajcp.12084,Distribution and Neighborhood Correlates of Sober Living House Locations in Los Angeles,2016.0,American Journal of Community Psychology,A. Mericle; K. Karriker-Jaffe; Shalika Gupta; Dave Sheridan; D. Polcin,"Sober living houses (SLHs) are alcohol and drug-free living environments for individuals in recovery. The goal of this study was to map the distribution of SLHs in Los Angeles (LA) County, California (N=260) and examine neighborhood correlates of SLH density. Locations of SLHs were geocoded and linked to tract-level Census data as well as to publicly available information on alcohol outlets and recovery resources. Neighborhoods with SLHs differed from neighborhoods without them on measures of socioeconomic disadvantage and accessibility of recovery resources. In multivariate, spatially-lagged hurdle models stratified by monthly fees charged (less than $1400/month vs. $1400/month or greater), minority composition and accessibility of treatment were associated with the presence of affordable SLHs. Accessibility of treatment was also associated with the number of affordable SLHs in those neighborhoods. Higher median housing value and accessibility of treatment were associated with whether a neighborhood had high-cost SLHs, and lower population density was associated with the number of high-cost SLHs in those neighborhoods. Neighborhood factors are associated with the availability of S",12.0,https://europepmc.org/articles/pmc5033720?pdf=render
ef26ea4d40b8ff296727bed41ccf47bda06ee21d,10.3390/land10080879,Micro-Analysis of Price Spillover Effect among Regional Housing Submarkets in Korea: Evidence from the Seoul Metropolitan Area,2021.0,Land,Leeyoung Kim; Wonseok Seo,"This study examined the price spillover effect of housing submarkets in cities in the Seoul metropolitan area in South Korea by using the Granger causality test and vector autoregressive model (VAR). We found that housing prices showed a higher spillover effect within regions with similar housing market characteristics. Additionally, the spatial spillover of housing prices revealed a difference between sales price and jeonse price. The spillover of jeonse price was characterized by mutual influence among neighboring cities, while that of sales price was characterized by the influence being transferred in one direction hierarchically. Furthermore, the effects of housing price indicated a slight difference between sales price and jeonse price. Although jeonse price was mainly affected by a neighboring area (geographic boundary), sales price was more influenced by the city with the highest housing prices. Lastly, the housing price spillover tended to be expansive around the city with the highest price. These results suggest that housing price policies targeting specific regions or areas in Korea must be differentiated according to the type of occupancy (jeonse or sales), and it is ess",12.0,https://www.mdpi.com/2073-445X/10/8/879/pdf?version=1629539437
f997df888ed6b26ae76370363c48e7bbc4000b13,10.1007/s42979-024-03578-7,Optimizing Polynomial and Regularization Techniques for Enhanced Housing Price Prediction Accuracy,2025.0,SN Computer Science,Preethi; D. Murthy; Vani A. Hiremani; Raghavendra M. Devadas; R. Sapna,"This study investigates the effectiveness of various regression models for predicting housing prices using the California Housing dataset. The models evaluated include Linear Regression, Ridge Regression, Best Polynomial Ridge Regression, Lasso Regression, Elastic Net Regression, and Support Vector Regression (SVR) with an RBF kernel. The analysis reveals that SVR with an RBF kernel exhibits the poorest performance, characterized by the highest Mean Squared Error (MSE) and the lowest R² score, indicating limited effectiveness for this dataset. Conversely, Linear Regression, Ridge Regression, and Best Polynomial Ridge Regression demonstrate significantly lower MSE values and nearly identical R² scores, each explaining approximately 60% of the variance in housing prices. Among these, Best Polynomial Ridge Regression marginally outperforms Linear and Ridge Regression, suggesting that including polynomial features enhances model performance. Although Lasso Regression shows slightly higher MSE than the leading models, it still performs better than Elastic Net and SVR. Overall, the study identifies linear regression, ridge regression, and best polynomial ridge regression as the most effe",12.0,https://link.springer.com/content/pdf/10.1007/s42979-024-03578-7.pdf
96726fe02beb31f372d7c80904ad281804989b1c,10.3390/ijgi11090477,"Spatial Interaction Analysis of Shared Bicycles Mobility Regularity and Determinants: A Case Study of Six Main Districts, Beijing",2022.0,ISPRS Int. J. Geo Inf.,Lu-Jin Hu; Zheng Wen; Jian Wang; Jing Hu,"Understanding the regularity and determinants of mobility is indispensable for the reasonable deployment of shared bicycles and urban planning. A spatial interaction network covering streets in Beijing’s six main districts, using bike sharing data, is constructed and analyzed. as Additionally, the exponential random graph model (ERGM) is used to interpret the influencing factors of the network structure and the mobility regularity. The characteristics of the spatial interaction network structure and temporal characteristics between weekdays and weekends show the following: the network structure on weekdays is obvious; the flow edge is always between adjacent blocks; the traffic flow frequently changes and clusters; the network structure on weekends is more complex, showing scattering and seldom changing; and there is a stronger interaction between blocks. Additionally, the predicted result of the ERGM shows that the influencing factors selected in this paper are positively correlated with the spatial interaction network. Among them, the three most important determinants are building density, housing prices and the number of residential areas. Additionally, the determinant of financ",12.0,https://www.mdpi.com/2220-9964/11/9/477/pdf?version=1662124546
8d561f74e01deb51dbaf047e04e8cbf395cd5191,10.1007/s12205-019-5885-y,Short-term Forecast Model of Apartment Jeonse Prices using Search Frequencies of News Article Keywords,2019.0,KSCE Journal of Civil Engineering,Hojun Kang; Kanghyeok Lee; D. Shin,"Housing prices, including Jeonse prices, are impacted by human psychological attitudes. News articles are one of the factors that influence such human psychological attitudes. Previous studies have confirmed the potential for utilization of news articles in the analysis of the real estate market. However, no actual studies on real estate price forecasting through news articles have yet been carried out. Accordingly, the present study proposes a short-term forecast model of apartment Jeonse prices through big data analysis of news articles. The forecast model was a regression model using Jeonse prices for apartments in the Pangyo area as the dependent variable and the Internet search frequency of news article keywords as the independent variable. The news article-based forecast model was created from the independent variable made as a combination of 4 keywords with a time shift of 10 months. Comparison with time-series models frequently used for real estate price forecasting showed that the model was superior in terms of forecast accuracy and forecast lead time. It is expected that use of the news article-based forecast model according to the present study will allow for the establi",12.0,https://doi.org/10.1007/s12205-019-5885-y
a1c52d0cdfa16b8b76a52eb8dda2994a02c6be3a,10.1111/iere.12366,CITY EQUILIBRIUM WITH BORROWING CONSTRAINTS: STRUCTURAL ESTIMATION AND GENERAL EQUILIBRIUM EFFECTS,2018.0,International Economic Review,Amine Ouazad; Romain G. Ranciere,"This paper develops a general equilibrium model of location choice with social interactions where mortgage approval rates determine household-specific choice sets that differ across neighborhoods and years in observable and unobservable dimensions. Existence and local uniqueness of city equilibria enable comparative statics estimates of the impact of changes in borrowing constraints on neighborhood-level prices and demographics. Estimation the model using micro data on property transactions, household demographics, neighborhood amenities, mortgage applications, and bank liquidity for the San Francisco Bay area, reveals that the price sensitivity of borrowing constraints explains about two-thirds of the price elasticity of neighborhood demand. General equilibrium estimates of the impact of the relaxation of lending standards on prices and neighborhood demographics bring two out-of-sample predictions for the period 2000-2006: (i) an increase in house prices accompanied by a compression of the price distribution and (ii) a reduction in the isolation of Whites in line with evidence of gentrification in the San Francisco Bay. Both predictions are supported by empirical observation.",12.0,http://www.nber.org/papers/w23994.pdf
ec99b6e8a7c10994a8eb24054f21d76bbe7cf3ae,10.17016/feds.2018.064,Measuring Aggregate Housing Wealth: New Insights from an Automated Valuation Model,2018.0,Finance and Economics Discussion Series,J. Gallin; Raven S. Molloy; Eric Nielsen; Paul A. Smith; K. Sommer,"We construct a new measure of aggregate U.S. housing wealth based on Zillow?s Automated Valuation Model (AVM). AVMs offer advantages over other methods because they are based on recent market transaction prices, utilize large datasets which include property characteristics and local geographic variables, and are updated frequently with little lag. However, using Zillow?s AVM to measure aggregate housing wealth requires overcoming several challenges related to the representativeness of the Zillow sample. We propose methods that address these challenges and generate a new estimate of aggregate U.S. housing wealth from 2001 to 2016. This new measure provides insights into some of the disadvantages of other approaches to measuring housing wealth. Specifically, with respect to the owner valuations typically used in survey data, it appears that homeowners were slow to recognize the drop in housing wealth during the financial crisis and that their estimates of this drop were unrealistically small. At the same time, repeat-sales price indexes appear to overstate the extent of the drop in value between 2006 and 2011 and overstate the recovery thereafter.",12.0,https://doi.org/10.17016/feds.2018.064
9357477a8421c36420b4fbb6be89a50b36cce0a8,10.48550/arxiv.2409.05335,A Multi-Modal Deep Learning Based Approach for House Price Prediction,2024.0,arXiv.org,Md Hasebul Hasan; Maknun Jahan; Mohammed Eunus Ali; Yuan-Fang Li; T. Sellis,"Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to pred",12.0,
5553871ab32e8f211d1140afba1971168295653e,10.1515/bog-2015-0013,Analysing the local geography of the relationship between residential property prices and its determinants,2015.0,,M. Dziauddin; Kamarul Ismail; Z. Othman,"Abstract This paper analyses the local geography of the relationship between residential property prices and its determinants. A semiparametric geographically weighted regression (S-GWR) technique is employed to explore this relationship. Selling prices, structural and locational attributes data were collected from the database of the Department of Valuation and Services of Malaysia, selected maps and reports. The outcome of this paper shows a strong geographically varying relationship between residential property prices and its determinants in which the residential property price determinants have a positive impact on prices in some areas but negative or no impact on the others. The magnitude of the effect is also found to be geographically varied; the capitalisation in residential property prices is found greater in some areas but less or with no effect in some other parts of the areas. The use of S-GWR technique makes it possible to reveal such geographically varying relationships, thus leading to a better understanding of the relationship between residential property prices and its determinants.",12.0,https://apcz.umk.pl/BGSS/article/download/bog-2015-0013/5318/17132
9d9bcef30c1235c7c33ac983a1571b2115b1b338,10.1080/14445921.2019.1626542,"Analysing the desired quality of housing in the Klang Valley region, Malaysia",2019.0,Pacific Rim Property Research Journal,J. Yap; Pui-Si Yong; M. Skitmore,"ABSTRACT Housing is crucial for realising the basic requirements of people; it provides a shelter and people spend most of their daily activities in the locality. This study seeks to examine the quality attributes influencing the value of housing and their effect on housing prices according to generation cohorts and affordable housing prices. Twenty-one housing quality factors are first identified by a literature search, with a questionnaire survey ranking safety considerations, air quality, location choice, house ventilation, and accessibility to transportation services to be the highest influence. A further factor analysis reveals five major underlying dimensions of these attributes vis-à-vis locational qualities, values and lifestyles, the availability of public amenities, surrounding environment and product uniqueness, and accessibility. The study’s contribution lies in its examination of the housing dimensions affecting quality of life along with providing insights and guidelines for emerging property developers in planning housing development projects in Malaysia and beyond.",12.0,https://eprints.qut.edu.au/131814/2/131814.pdf
6d193a51bb53fb995b013f3471217f1ed762ad31,10.3390/jrfm15030139,Spatial Analysis and Modeling of the Housing Value Changes in the U.S. during the COVID-19 Pandemic,2022.0,Journal of Risk and Financial Management,Xinba Li; C. Kao,"COVID-19 has affected almost all sectors of the economy, including the real estate markets across different countries in the world. A rich body of literature has emerged in analyzing real estate market trends and revealing important information. However, few studies have used a spatial perspective to investigate the impact of COVID-19 on property values. The main purposes of this study are as follows: (1) to explore the spatial distribution and spatial patterns of housing price changes during the COVID-19 pandemic crisis in the U.S. real estate market and (2) to model the spatially nonstationary relationships between the housing price change and COVID-19 characteristics. We find that housing price changes differ across space and appear associated with the spatial distribution of the COVID-19 case rates. The housing market volatility is amplified by the uneven distribution of some socioeconomic factors. The spatially uneven housing price changes may bring an uneven spillover effect to the rest of the economy and lead to divergence in economic growth across different areas.",12.0,https://www.mdpi.com/1911-8074/15/3/139/pdf?version=1647428921
a303f771010075203c591e922cc1f63675cf8818,10.1080/09599916.2023.2206823,Automated valuation models: improving model performance by choosing the optimal spatial training level,2023.0,Social Science Research Network,Bastian Krämer; Moritz Stang; Vanja Doskoč; W. Schäfers; Tobias Friedrich,"ABSTRACT The academic community has discussed using Automated Valuation Models (AVMs) in the context of traditional real estate valuations and their performance for several decades. Most studies focus on finding the best method for estimating property values. One aspect that has not yet to be studied scientifically is the appropriate choice of the spatial training level. The published research on AVMs usually deals with a manually defined region and fails to test the methods used on different spatial levels. Our research aims to investigate the impact of training AVM algorithms at different spatial levels regarding valuation accuracy. We use a dataset with 1.2 million residential properties from Germany and test four methods: Ordinary Least Square, Generalised Additive Models, eXtreme Gradient Boosting and Deep Neural Network. Our results show that the right choice of spatial training level can significantly impact the model performance, and that this impact varies across the different methods.",12.0,https://doi.org/10.2139/ssrn.4272379
3e9e9ffcd989ab7442b8cd95e1ce769654ace34f,10.1007/s10037-024-00210-2,Aging and real estate prices in Germany,2024.0,Review of Regional Research,P. Breidenbach; P. Jäger; Lisa Taruttis,"Exploiting regional heterogeneity in population dynamics across more than 10,000 municipalities in Germany, we provide robust empirical evidence that population aging depresses real estate prices and rents. Using millions of individual real estate listings and detailed demographic data at the municipality level, we estimate that average sales prices in 2020 would have been up to 12% higher if the population age distribution had been the same as in 2008. We show that population aging not only reduces prices but also increases the availability of real estate. In addition, we document substantial heterogeneity in price responses across dwelling type, property characteristics, and urban-rural status, suggesting that lower housing demand and life-cycle dissaving are driving our results. We predict that population aging will continue to exert downward pressure on house prices and exacerbate regional disparities in Germany.",12.0,https://doi.org/10.1007/s10037-024-00210-2
de9df99097a91a951cee948f2d10c566362746d6,10.1080/17421772.2023.2242897,Comparing modelling performance and evaluating differences of feature importance on defined geographical appraisal zones for mass real estate appraisal,2023.0,Spatial Economic Analysis,A. Aydinoglu; Süleyman Şişman,"ABSTRACT The features influencing real estate value in different residential areas and cities are important for spatial economic analysis besides high appraisal accuracy. In this study, a methodology was developed for computer-assisted mass real estate appraisal with a case study implemented through the use of big geographical datasets including 121 features and around 200,000 samples of real estate in Istanbul and Kocaeli (Turkey). Prediction models using the random forest technique were developed for five appraisal zones determined with spatially constrained multivariate clustering. With machine learning and mass appraisal metrics, modelling performance improves in appraisal zones with a lower standard deviation expressing real estate value in neighbourhoods. Since importance levels and ranks of features vary in zones, the mass appraisal should be done with a sufficient number of features.",12.0,https://figshare.com/articles/journal_contribution/Comparing_modelling_performance_and_evaluating_differences_of_feature_importance_on_defined_geographical_appraisal_zones_for_mass_real_estate_appraisal/24069594
a148ca562a43898b5b02881f2141ceed866909da,10.1093/jeg/lbad009,Quantifying land-use regulation and its determinants,2023.0,Journal of Economic Geography,Simon Büchler; Maximilian V. Ehrlich,"
 We analyze land-use regulation and the determinants thereof across Swiss municipalities. We construct several residential development stringency indices based on a comprehensive survey. These indices capture various aspects of local regulation and land-use coordination across jurisdictions. Combining these indices, we construct an index that provides harmonized information about what local regulation entails and the local regulatory environment across municipalities. Our analysis shows that historical building density, natural amenities, socio-demographic factors, cultural aspects and municipal competition are important determinants of local land-use regulation. However, a large share of land-use regulation variation remains unexplained. Moreover, our results indicate that more stringent land-use regulation is associated with steeper house price increases but less urban sprawl.",12.0,https://doi.org/10.2139/ssrn.4066700
5ea1493754d508bc09bf1895186f7e6ac8bb1709,10.18267/j.pep.340,Housing price bubble analysis - case of the Czech republic,2009.0,,Jan Čadil,"The paper deals with the hypothesis of housing price bubble in the Czech economy. This topic is very popular among economists worldwide now, especially because of the U.S. housing crisis and subsequent collapses on financial markets. However, surprisingly there are not many analyses dealing with the Czech housing market (besides e.g. very brief Financial Stability Report published by the Czech National Bank in 2008) and with the possible housing bubble burst. The first standard bubble indicators like P/I ratio are used to identify the bubble possibility on the Czech housing mar- ket. As the second step a regression analysis (VAR model) is being used for deeper analysis of the situation. The whole analysis is complicated by a lack of relevant data and quite short-time series.",12.0,http://pep.vse.cz/doi/10.18267/j.pep.340.pdf
730cc2d0bb3920ae40df7b3b27521a940441caf8,10.1016/j.landusepol.2021.105922,Non-linear and weakly monotonic relationship between school quality and house prices,2022.0,Land Use Policy,S. Mathur,"This study provides evidence for a non-linear and weakly monotonic relationship between school quality and house prices. Using Fremont, California, as the study area, the regression analysis shows that homeowners are unwilling to pay a premium for an increase in school quality from low to medium quality. However, they are willing to pay a) a large premium when all schools are top-quality schools and b) a premium for access to nationally-renowned schools, which is in addition to the premium for top-quality schools. These findings have important land use policy significance because they provide new insights into the homeowner’s residential location choice and highlight the need to consider school quality in a jurisdiction’s land use and zoning decisions.",12.0,https://www.sciencedirect.com/science/article/pii/S0264837721006451/pdf
a66db69458a44f92f31adcc0148e095d23d9c763,10.1016/j.cities.2020.102753,Measuring public service quality: Revisiting residential location choice using emergy synthesis of local governments in Pennsylvania,2020.0,,Jae Min Lee; W. Braham,"Abstract In this article, we present a study of local governments in the Commonwealth of Pennsylvania measuring public service quality and revisiting residential location choice. This study adopts “emergy” (spelled with an “m”), a relatively new unit of measuring energy quality, to quantify the quality of public service. The concept of neighborhood choice has evolved to incorporate distance to center, transportation modes and commute time, match between household income and housing price, availability of public services and education, and own-group preference by ethnic or socioeconomic status. Despite the quality of public service by local governments being identified a critical factor affecting residential location choice, it has received less attention because of the nebulous concept and immeasurable characteristics. The emergy analysis of a local government system allows for testing of incompatible units of a socio-economic phenomenon. This study confirms the conventional residential sorting theory that people tend to travel further for cheaper houses and for better education, but people do not travel further for a higher quality of hard infrastructure such as road network and m",12.0,
2fbb3a58c956f382827e6bbb2455f777f5f01d19,10.2139/ssrn.3833734,House Price Prediction Using Multiple Linear Regression,2021.0,,Anirudh Kaushal; Achyut Shankar,"There comes a point in everyone’s life when the person wishes to buy or sell a house. First consider a scenario where a person needs to buy a house. The person will look for his/her desired house for a sensible price tag. The person will have some features decided what he/she wants to have in the house. The person will be able to decide whether the type of house he/she is looking for is worth of the price or not. 
Similarly, consider a scenario where a person needs to sell a house. By making use of the house price prediction system, the seller would be able to decide what all features he/she could add in the house so that the house can be sold for a higher price. Hence, from both the above scenarios we can confirm that house price prediction is useful both for the buyer and seller. 
This paper will help to predict the house prices based on various parameters. The users will be able to input the type of house they desire to buy and with the help of machine learning the house price predictor will display the estimated price of the desired house.",12.0,
742b568f6e3d1873a27d435633298d37b98705f9,10.1080/14036096.2019.1672577,The Relationship between Housing Prices and Market Transparency. Evidence from the Metropolitan European Markets,2019.0,"Housing, Theory and Society",E. Ionașcu; Paloma Taltavull de La Paz; Marilena Mironiuc,"ABSTRACT This paper develops an empirical analysis quantifying the housing prices reaction to market transparency in 21 European metropolitan areas for the period 2004–2016. Market transparency is measured by the Global Real Estate Transparency Index (JLL) with its five dimensions. Applying a biannual panel EGLS model regression with fixed effects by cities as on data collected from several resources and measuring transparency using JLL index, results quantify the housing price elasticity of responses to changes in transparency. Results indicate that a decrease in transparency level is associated with an increase in house prices. The effect of transparency on the housing prices is heterogeneous in European metropolises, with most visible impacts in the Eastern metropolitan areas (Bratislava, Bucharest, Warsaw and Zagreb) but also in Western areas such as Copenhagen, Dublin and Madrid. London, Paris and Amsterdam also show how large transparency contributes to low house price increase, as the market fundamentals support.",12.0,
4f9246683d1dfbd13615e4cb7e0ad48997864ebe,10.1177/13548166231180417,The impact of the tourism accommodation composition on housing prices: The case of Croatia,2023.0,Tourism Economics,Maruška Vizek; Tajana Barbić; A. Č. Časni,"The adverse effects booming tourism activity and peer-to-peer platforms on housing markets are well known, while the influence of relative changes in tourism accommodation composition on housing prices are not well understood. To shed more light on the issue, this paper employs the data set on housing prices and its main tourism, economic, and demographic determinants, for cities and municipalities in a tourism-dependent country. The results suggest more intensive tourism demand and the conversion of housing stock into rentals boost housing prices. The increase in the share of short-term rentals depresses prices, while in destinations where hotels and campsites become more prevalent prices increase. These findings could be attributed to the pricing-in effects of an increased supply of tourism amenities developed as part of hotels’ and campsites‘ product mix that improve the quality of life, and lower quality of life experienced at destinations where rentals are becoming more prevalent.",12.0,
8899d011bc19c489315ccc358d4767c58bec462c,10.1108/jfmpc-02-2024-0011,Rental price index forecasts of residential properties using Gaussian process regressions,2025.0,Journal of Financial Management of Property and Construction,Bing-Zi Jin; Xiao-Jie Xu,"

Since the Chinese real estate market has expanded so quickly over the past 10 years, investors and the government are both quite concerned about projecting future property prices.



This work aims to investigate monthly rental price index forecasts of residential properties for ten major Chinese cities from 3M2012 to 5M2020 by using Gaussian process regressions with a diverse variety of kernels and basis functions. The authors conduct forecast exercises through use of Bayesian optimizations and cross-validation.



With relative root mean square errors spanning the range of 0.0370%–0.8953%, the constructed models successfully forecast the ten price indices from 6M2019 to 5M2020 out of sample.



The findings might be used independently or in combination with other projections to create theories about the trends in the rental price index of the residential property and carry out additional policy analysis.
",12.0,
5173f612fa22192867f403eb6863c58bdf3eea93,10.18282/i-s.v3i1.353,The Forecast of Housing Price in Xi’an Based on Big Data Analysis,2020.0,,Zhi-Yuan Guo,"Based on the statistical data, the forecast for housing price of Xi’an city is made by identifying 13 different kinds of indicators. The multi-variable regression model and SPSS are used to analyze the data in linear and non-linear way respectively. R2 for both methods are over than 0.9. So we can get the similar conclusion that housing price in Xi’an will not increase dramatically recently and keep stable.",12.0,http://insight.piscomed.com/index.php/I-S/article/download/353/265
cecea371ec1a3bc249deb4efa8d87d3bc926e15c,10.1080/00036846.2020.1838432,Price dynamics of China’s housing market and government intervention,2020.0,,Yu Jiang; Yu Wang,ABSTRACT China’s housing sector offers an interesting case study of relationship between government interventions and price dynamics. Frequent and intensive government interventions create a highly fluctuant housing market in China. This paper applies a structural break model to investigate the dynamics in the evolution of China’s housing price between 1998 and 2016. Results show that the development of China’s real estate market exhibits much richer characteristics of structural changes rather than simple switching between boom and burst. Further studies indicate that government interventions is the most important source of structural changes in the dynamics of China’s housing market because of the inherent conflict between policy aims of market stability and rapid economic development mainly backed up by the housing sector.,12.0,
89e297cc6f8d3eccaad4811d5d584356d8e26414,10.1109/icmnwc56175.2022.10031728,House Prices Prediction Using Machine Learning Regression Models,2022.0,2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC),Amit Gupta; S. K. Dargar; Abha Dargar,"Understanding request drifts is critical in today’s booming civilization and ever-changing request settings. The current vaticination of house prices corresponds to the study’s top substance patterns. Property values are of tremendous relevance to consumers as merchandisers, and casing prices are an essential representation of frugality. Property is one of our ecosystem’s lowest quantum transparent diligences. The major goal of this investigation is to predict housing prices using real-time factors. The results showed that this strategy produces less inaccuracy and delicacy than individual algorithms. We also propose leveraging actual-time locality details from Google maps to obtain precise actual-world estimates. The former model, with its reduced treatment and information over-fitting, reduces.",12.0,
e92bb514e3258a302ceaf70b31697cccfb37fe95,10.1016/j.jhe.2021.101790,"Neighbourhood, school zoning and the housing market: Evidence from New South Wales",2021.0,Journal of Housing Economics,Firmin Doko Tchatoka; Vanessa Varvaris,"Abstract This paper investigates the impact of primary school zoning on housing prices in Australia. Using the combination of boundary and regression discontinuity design techniques, along with an index of school academic quality and comprehensive data on both schools and housing transactions in New South Wales we find that the price of houses located in high-performing side of primary school zone boundaries is, on average, about 2.5% to 3.6% higher than that of similar houses located in low-performing side of these boundaries. This finding provides not only an insight into the price elasticity of demand for high quality education, but also has important policy implications as it highlights the need to address the potential educational inequalities associated with school zoning in Australia.",12.0,
eb701cf86026ddf09cc2f6fa6112efb905086050,10.1109/icacccn51052.2020.9362864,House Price Prediction Using Optimal Regression Techniques,2020.0,"2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)",V. S. Rana; Jayanto Mondal; Annu Sharma; Indu Kashyap,"Humans are very thoughtful when they want to make some investments, especially when it is a house. When people need a house, they focus on various factors but the most important is the budget. The motivation of this research work is to predict the house prices for lower- and middle-class people based on their financial parameters. This paper compared the results of different regression techniques like Support vector regression, XGBoost, Decision tree regression, and Random forest regression, and based on their performances, we have taken the best among them for optimally predicting the house prices. To train the models, we have focused on various parameters that affect the costs of a house like physical condition, location, amenities, interest rates, etc.",12.0,
5e3ab6608944f5e0b99b9e3bfbbee327f13097ae,10.1093/jofore/fvy046,Review of the Effects of Conservation Easements on Surrounding Property Values,2018.0,Journal of Forests,T. Reeves; B. Mei; P. Bettinger; J. Siry,"This paper reviews the effects of conservation easements (CEs) on surrounding property values. From the literature, key variable characteristics of CEs that influence surrounding property value are development potential, proximity, and the effect of forest composition and characteristics. Overall, proximity to CEs was found to increase surrounding property values. The tax implications of CEs were also examined. The effect of municipal services, the effect of housing demand, and the potential for self-financing for additional CEs were discussed. CEs were largely found to increase tax revenue by increasing surrounding property values and have the potential for self-financing if designed properly.",12.0,
7fec8df376aeaa35253cefdcd59f21dfce8b29db,10.3828/jtep.2007.41.2.219,Discriminant Impact of Transit Station Location on Office Rent and Land Value in Seoul: An Application of Spatial Econometrics,2007.0,Journal of Transport Economics and Policy,Jin Kim,"This study examines 731 office properties in Seoul, Korea to determine discriminant impact on office rentals and land values by a transit station's location in the city. Value premiums from better accessibility to stations seem to exist; but they decay with increasing distance from centres and correlate with the development densities of station areas. Research sampled heavily from suburbs may not find the same station benefits as in the denser inner city. Also, this study expands its estimation model to contain errors as well as dependent variables, reducing the spatial autocorrelation and thereby producing a more efficient estimate for transit's impact.",12.0,
7b46a828ae3328415b07a84f540a43fcaec3d52c,10.1016/s1007-0214(05)70079-1,Influence of spatial features on land and housing prices,2005.0,,Xiao-Lu Gao; Yasushi Asami,,12.0,https://doi.org/10.1016/s1007-0214(05)70079-1
b70da36f6c27eb2e2931e1fd49b50ec864890563,10.1111/tesg.12067,Construction of a Spatial Housing Price Index by Estimating an Almost Ideal Demand System,2014.0,,Víctor Iturra; Dusan Paredes,,12.0,https://mpra.ub.uni-muenchen.de/30581/1/MPRA_paper_30581.pdf
bee52343ffa73aad23542914ec3ed1e1d03d6720,10.14455/isec.res.2016.122,ANALYSIS OF ECONOMIC DETERMINANTS OF AFFORDABLE HOUSING PRICES,2016.0,,A. Olanrewaju; Lim-Tat Lee; Seong Yeow Tan; N. Mine; Z. Nizamani; A. Aziz,,12.0,https://doi.org/10.14455/isec.res.2016.122
b6e75dff6727c5b0316020b1050e7a790688cd43,10.1016/j.jhe.2013.02.003,Estimating household demand for housing attributes in rent-controlled markets,2013.0,,J. V. van Ommeren; M. Zijl,,12.0,
6f273edd7d4d076ad74ff6c37cbb97f200161903,10.1016/j.apgeog.2017.01.007,"Housing-market bubble adjustment in coastal communities - A spatial and temporal analysis of housing prices in Midwest Pinellas County, Florida",2017.0,,Yin-Hsuen Chen; T. Fik,,12.0,
df9bd6aab9c25a764585548d28c338ac1b397458,10.1061/(asce)up.1943-5444.0000795,Influence of Accessibility to Urban Service Amenities on Housing Prices: Evidence from Beijing,2022.0,Journal of urban planning and development,Chao Zhang; Mimi Xiong; Xuehui Wei,,12.0,
7b1ba7a767f823276c2dbc08652bcda2752d6b82,10.1016/j.regsciurbeco.2016.07.011,Urban house price surfaces near a World Heritage Site: Modeling conditional price and spatial heterogeneity,2016.0,,Markus Fritsch; Harry Haupt; Pin T. Ng,,12.0,
2796ce3bcafdb17fdfb1fb9ade9b84d23715f4d1,10.1016/j.jclepro.2022.132982,Impact of proximity to thermal power plants on housing prices: Capitalizing the hidden costs of air pollution,2022.0,Journal of Cleaner Production,I. Tsai,,12.0,
26a86fd4dd9609b708b509ba87965698266d7b6d,10.1080/10835547.2015.12092093,School Vouchers and Home Prices: Premiums in School Districts Lacking Public Schools,2015.0,,S. Cannon; Bartley R. Danielsen; David M. Harrison,,12.0,
6ca0c8ff0d8edf852b45543319e01f16d037cdf2,10.1080/07293682.2016.1210659,Atlas of cities,2014.0,,Michael Bounds,"Atlas of Cities is a weighty A4 size hardback that will not be out of place on your coffee table. The blurb on the back describes it as visually captivating and a vibrant book from which the ‘would-be urbanist’ can learn something. I was attracted to the title as it is edited by Paul Knox, author of Urban Social Geography and many other insightful and informative texts. Still, on reading the book, I was puzzled as to where it was targeted. The book is divided into 13 varieties of cities and might well be an adjunct text or ideas starter in a course on City Form and Function. The chapters sequentially address the Greek and Roman foundations of theWestern City, Mercantile Cities, Imperial Cities, Industrialisation, Modernity, Globalisation, Postmodern Cities, Megacities, Planned Cities, with chapters 10 and 11 given to Economic and Cultural Globalisation and 12 and 13 to Sustainable Urbanism. Nowhere is it made clear exactly what disciplinary vantage point is being adopted; it thus lacks a clear structure or set of debates about cities as an organising principle. Each chapter identifies a core city and secondary cities: for example, the core Industrial city is Manchester, while secon",11.0,
ce8ead67967e98320e1e3922fe48feba306e0576,10.1108/ijhma-02-2020-0012,Portland ordinances: tiny home and short-term rental permits,2020.0,,B. Brotman,"
Purpose
This study aims to examine the permit changes enacted by the city of Portland, Oregon, USA, on the construction and subsequent short-term rental of tiny homes. The permitting process was eased by the city in 2014. The city’s enforcement of occupancy and rental ordinances, sometimes called Airbnb laws, were tightened in 2019. The new code restrictions are tighter than the rental codes that existed previously.


Design/methodology/approach
This paper uses time-series data to first consider the thesis that relaxing building permit requirements for tiny homes has encouraged legal construction and increased the number of applications filed with the city planning office. The number of permits was the dependent variable and time-sensitive dummy variable was the independent variable. An adjusted T-statistic was calculated using a least-squares regression model with a moving average autocorrelation adjustment. The second regression model considers the financial relationship between active listings on Airbnb and HomeAway to a housing price coverage ratio and the aggregated dynamic-factor model used to calculate the economic activity index for Portland.


Findings
There were two repo",11.0,
daaf3309a526e7d04a6bb72f85bd00997c0ae1c9,10.15396/eres2001_300,Transport Policy and Property Values,2001.0,,M. Wrigley,"Transport and environmental policy in the European Union and within the UK is increasingly concerned with the harmful effects of traffic. Stringent controls on car use in particular are being proposed and implemented. In the UK, for example, these controls include the exclusion of cars from certain streets and areas, road pricing and taxing of workplace parking. These restrictions must be implemented in parallel with investment in public transport in order to maintain levels of accessibility. Accessibility is a key determinant in the location decisions for a range of land uses. There is growing interest among policy-makers in how transport policy and the general rise in traffic congestion may affect land and property values by altering the pattern of accessibility in terms of transport mode, infrastructure provision and cost. In terms of transport influence on land and property values, new transport infrastructure may increase residential property values due to improved accessibility and increase commercial property values due to improved accessibility and possible agglomeration benefits. Previous research has suggested that lower transport costs will result in higher land and prop",11.0,https://eres.architexturez.net/system/files/pdf/eres2001_300.content.pdf
df9b350f2ff9b9e69d0b4d167d736fd69935efa7,10.3390/su132313477,"Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China",2021.0,Sustainability,Yang Wang; Xiao-Li Yue; Hong'ou Zhang; Yong-Xian Su; Jing Qin,"The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristic",11.0,https://doi.org/10.3390/su132313477
f05ee7f22eb3fb798a64325ca17090ebc5f379f1,10.1108/ijhma-12-2019-0125,"Volatility clustering, risk-return relationship and asymmetric adjustment in the Finnish housing market",2020.0,,Josephine Dufitinema,"The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types.,Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas.,Results reveal that clustering eff",11.0,https://osuva.uwasa.fi/bitstreams/35288282-e8dd-4647-b41a-031160903885/download
5ed97c04c46a8455a01a5d7109d10c7beb4061f8,10.3390/su12208701,House Prices in the Peripheries of Mass Rapid Transit Stations Using the Contingent Valuation Method,2020.0,Sustainability,Kuo-Cheng Hsu,"With the implementation of growth management planning in urban areas and the realization of sustainable development visions, transit-oriented development has become a form of mainstream urban development. Relevant studies have verified that the market prices of houses in the peripheries of public transit stations are higher than those of regular houses. However, when buying a house, people make price decisions on the basis of their levels of identification with the amenities and environmental qualities of residential locations. The question arises whether current housing price levels in the peripheries of public transit stations properly reflect or over-reflect this consideration. To clarify this, this study selected the peripheries of mass rapid transit (MRT) stations in the Taipei metropolitan area in Taiwan as the research area and designed a willingness-to-pay questionnaire for houses in the peripheries of MRT stations by using the contingent valuation method. Subsequently, a Tobit regression model was established to estimate the prices that people are willing to pay for such houses. The results revealed that after the respondents had considered the advantages and disadvantages",11.0,https://www.mdpi.com/2071-1050/12/20/8701/pdf?version=1603197859
7479faf4f8f34c48a731da73a302614ced1386e5,10.4000/books.pufr.1110,Capitalisation immobilière des gains d’accessibilité : étude de cas sur l’agglomération lyonnaise,2006.0,Quatre ans de recherche urbaine 2001-2004. Volume I,Ghislaine Deymier,"Les infrastructures de transport generent des gains d'accessibilite qui determinent l'evolution de la forme urbaine. L'accroissement des vitesses de circulation et, par consequent, la diminution des temps de deplacement contribuent, dans le contexte urbain, a l'augmentation des destinations accessibles et donc a l'allongement des distances de deplacement. Lors de l'evaluation des effets d'une infrastructure de transport urbaine, il convient alors d'analyser les gains d'accessibilite plutot que les gains de temps afin de tenir compte de la relative constance des budgets-temps de transport. L'articulation entre le systeme de transport et les modes d'occupation des sols se fonde sur la relation entre les differentiels d'accessibilite et de prix immobiliers. Le marche immobilier est l'un des elements structurants de l'espace urbain. Par consequent, il est important de comprendre comment se forme le prix du logement et comment les activites s'organisent au sein de la ville en fonction des variations de ce prix. Le peripherique Nord de Lyon, a travers son inscription a la fois spatiale au cœur du marche immobilier de l'agglomeration lyonnaise et temporelle, offre un cadre ideal d'apprehe",11.0,http://books.openedition.org/pufr/1110
df1d51c0e9c7472e8c906c90bf3ff21a300546df,10.56294/dm2024425,Implication of Different Data Split Ratios on the Performance of Models in Price Prediction of Used Vehicles Using Regression Analysis,2024.0,Data and Metadata,Md Alimul Haque; Md Shams Raza; Sultan Ahmad; Md. Alamgir Hossain; H. Abdeljaber; A. E. M. Eljialy; Sultan Alanazi; J. Nazeer,"Introduction; Artificial intelligence (AI) and Machine Learning have become buzzwords lately due to technological changes and data quality testing, especially in shape and finish analysis. Lots of research has been conducted for linear regression algorithms to predict the price in different sectors for share stock, rental properties, prices of used cars etc. This study provides suitable data split ratio for optimum cost estimation based on linear regression model. In present days there is an increasing demand for having own car for every middle class family therefore this have given opportunity to motor vehicle business to offer wide range of used vehicle for re-sale especially companies like Maruti Suzuki, Tata motors & Mahendra motors in Indian motor vehicle industries. Therefore, it is important to know the current value of your car before spending your hard-earned money on any item. Objective; The objective of this paper is finding appropriate value of cars in Metropolitans or even in state capitals. Features like model, mileage, AC, seating capacities, fuel type automatic will be taken into account when doing this. This estimate is designed to help customers find the right opt",11.0,https://doi.org/10.56294/dm2024425
3184c093b8b8284f6c26ed51b28ad413014d735f,10.1108/jes-07-2023-0376,Factors determining default in P2P lending,2023.0,Journal of Economic Studies,Evangelia Avgeri; Maria Psillaki,"PurposeThe research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.Design/methodology/approachThe research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.FindingsThe results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed along",11.0,
8dfdf34bbdd20440fabd15c73256d21ef2bbde75,10.3390/su10072357,Location Recommendation of Digital Signage Based on Multi-Source Information Fusion,2018.0,Sustainability,Xiaolan Xie; Xun Zhang; Jingying Fu; Dong Jiang; Chongchong Yu; Min Jin,": With the increasing amount of digital signage and the complexity of digital signage services, the problem of introducing precise location recommendation methods for digital signage should be solved by digital signage enterprises. This research aims to provide a sustainable location recommendation model that integrates the spatial characteristics of geographic locations and multi-source feature data to recommend locations for digital signage. We used the outdoor commercial digital signage within the Sixth Ring Road area in Beijing as an example and combined it with economic census, population census, average house prices, social network check-in data, and the centrality of traffic networks that have an impact on the sustainable development of the regional economy as research data. The result shows that the proposed method has higher precision and recall in location recommendation, which indicates that this method has a better recommendation effect. It can further improve the recommendation quality and the deployment of digital signage. By this method, we can optimize resource allocation and make the economics and resources sustainable. The digital signage recommendation results of ",11.0,https://www.mdpi.com/2071-1050/10/7/2357/pdf?version=1530875131
e1cfc6533708817248f620880f158c76a6fc1bd3,10.1016/j.jglr.2022.04.016,Human well-being and natural capital indictors for Great Lakes waterfront revitalization.,2022.0,Journal of Great Lakes research,T. Angradi; Jonathon Launspach; M. Wick,"Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home o",11.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642259
239ad5c501bf0490bec44e8c3aac5e374629d6a2,10.1590/0037-8682-0563-2019,"Spatial diffusion of Zika fever epidemics in the Municipality of Salvador-Bahia, Brazil, in 2015-2016: does Zika fever have the same spread pattern as Dengue and Chikungunya fever epidemics?",2020.0,Revista da Sociedade Brasileira de Medicina Tropical,Laís Santos Santana; J. Braga,"Abstract INTRODUCTION The recent emergence and rapid spread of Zika and Chikungunya fevers in Brazil, occurring simultaneously to a Dengue fever epidemic, together represent major challenges to public health authorities. This study aimed to identify and compare the 2015-2016 spatial diffusion pattern of Zika, Chikungunya, and Dengue epidemics in Salvador-Bahia. METHODS We used two study designs comprising a cross-sectional-to-point pattern and an ecological analysis of lattice data. Residential addresses involving notified cases were geocoded. We used four spatial diffusion analysis techniques: (i) visual inspection of the sequential kernel and choropleth map, (ii) spatial correlogram analysis, (iii) spatial local autocorrelation (LISA) changes analysis and, (iv) nearest neighbor index (NNI) modeling. RESULTS Kernel and choropleth maps indicated that arboviruses spread to neighboring areas near the first reported cases and occupied these new areas, suggesting a diffusion expansion pattern. A greater case density occurred in central and western areas. In 2015 and 2016, the NNI best-fit model had an S-curve compatible with an expansion pattern for Zika (R2 = 0.94; 0.95), Chikungunya ",11.0,http://www.scielo.br/pdf/rsbmt/v53/1678-9849-rsbmt-53-e20190563.pdf
46b02e133f37c6b822199a3f1f10fd7caaf03785,10.1080/24694452.2019.1667219,Building Hierarchies of Retail Centers Using Bayesian Multilevel Models,2020.0,Annals of the American Association of Geographers,Sam Comber; Daniel Arribas-Bel; A. Singleton; Guanpeng Dong; Les Dolega,"The perceived quality of urban environments is intrinsically tied to the availability of desirable leisure and retail opportunities. In this article, we explore methodological approaches for deriving indicators that estimate the willingness to pay for retail and leisure services offered by retail centers. Most often, because the quality of urban environments cannot be qualified by a natural unit, the willingness to pay for an urban environment is explored through the lens of the residential housing market. Traditional approaches control for individual characteristics of houses, meaning that the remaining variation in the price can be unpacked and related to the availability of local amenities or, equivalently, the willingness to pay. In this article, we use similar motivations but exchange housing prices for residential properties with property taxes paid by nondomestic properties to glean hierarchies of retail centers. We outline the applied methodological steps that include very recent, nontrivial contributions from the literature to estimate these hierarchies and provide clear instructions for reproducing the methodology. Using the case study of England and Wales, we undertake a",11.0,https://doi.org/10.1080/24694452.2019.1667219
aa609d114d978812c7d85d331a01f2c52bcc6633,10.1108/ijhma-01-2022-0015,Housing affordability: an econometric framing for policy discussions,2022.0,International Journal of Housing Markets and Analysis,Yener Coskun,"
Purpose
The purpose of this study is to analyze short- and long-run market-sensitive drivers of housing affordability. The study highlights an ongoing housing affordability crisis in an emerging market context by also providing an empirical tool to combat the crisis.


Design/methodology/approach
To investigate determinants of uniquely constructed effective housing affordability index and house price to income ratio index, the author uses a bound testing approach to cointegration and error correction models, besides causality tests, variance decompositions and impulse response functions. This study uses Turkish data for the period of 2007 M06 and 2017 M12.


Findings
The evidence suggests that the housing affordability crisis is mainly driven by credit expansion, rent and construction costs. A sensible housing policy response would target these variables. This evidence suggests that housing affordability mostly depends on housing market dynamics rather than policies because of the exogeneous/cyclical natures of the drivers.


Research limitations/implications
Data constraints shape the study. A regional or an aggregate-level panel study cannot be developed because of a lack of dat",11.0,
9d9c8cd90b0ea6afe016dcfe47c8b7f0e63bb2a5,10.1080/15563650802022839,Area-level socioeconomic status in relation to outcomes in γ-hydroxybutyrate intoxication,2009.0,Clinical toxicology,I. B. Anderson; S. Kim-Katz; J. Dyer; G. Earnest; John P. Lamb; P. Blanc,"Background. Area-level socioeconomic status (SES) may play an important role in drug abuse patterns, including related health outcomes. This may be particularly relevant for γ-hydroxybutyrate (GHB), which is prototypical of “party” drug abuse. Methods. We retrospectively reviewed GHB-related cases reported to the California Poison Control System (CPCS; January 1, 1999 through June 30, 2007). We limited analysis to CPCS calls containing a residential zip code (ZC). The CPCS data were extracted for key case characteristics, including the residential ZC. We linked cases to corresponding 2000 U.S. Census data for area-level measures of SES and demographics. We used multiple logistic regression analysis to test the associations between area-level SES and GHB case severity, taking into account area-level demographics and individual-level GHB high-risk behaviors. Results. We analyzed 210 cases. Taking into account area-level demographics (age and racial mix; urbanicity) and GHB-related high-risk behaviors (use of GHB congeners; GHB-dependence; co-ingestion of other agents), we associated higher area-level SES with greater GHB case severity. There was 40% increased likelihood of major GHB ",11.0,https://www.ncbi.nlm.nih.gov/pmc/articles/2763576
e4d052995cfc686cb8c33e0b201f180f2846b717,10.1057/s41599-024-03110-5,Establishment of an equity evaluation method for urban parkland layout under the process-outcome perspective,2024.0,Humanities and Social Sciences Communications,Yu-Han Guo; Yi-Lun Cao; Yue Li; Runjue Wang; Yu-Hao Fang,"Urban parkland, as a critical component of urban green space, reflects the distribution of public resources. Combined with the concept of the 15-min Living Circle, this paper comprehensively evaluated the equity of urban parkland in the Central Area of Zhengzhou from a process-outcome perspective and established an evaluation method for assessing the equity of parkland in high-density urban areas. The objective of this approach is to provide a systematic and applicable evaluation method for metropolises that is more comprehensive than traditional evaluation methods, which rely solely on accessibility. In terms of process equity, spatial equity values were calculated using the Three Step Catchment Area Method (3SFCA) with walking and bicycle travel models. For outcome equity, Zone Entropy was utilized with demographic characteristics. The results illustrated that the overall accessibility and equity of urban parkland layout in the Central Area of Zhengzhou were inferior, with an extensive number of blind service areas and serious polarization. The Zone Entropy varied considerably among streets, which indicated significant differences in the allocation of parkland and population. Fur",11.0,http://dx.doi.org/10.1057/s41599-024-03110-5
2465540e2f7148e3e057f28da1038521a4543320,10.3390/su11041142,Workplace Parking Provision and Built Environments: Improving Context-Specific Parking Standards Towards Sustainable Transport,2019.0,Sustainability,Qian Liu; Mingjian Zhu; Zuo-Peng Xiao,"Both academics and practitioners concur that parking restraints should be actively introduced in urban centers and job-intensive areas, to prevent overdependence on automobiles. Many Chinese metropolises have reduced the level of parking minimums for non-residential premises in central and transit-rich areas. However, there is a lack of research examining the effectiveness of these policies. Taking Shenzhen as a case study, this paper compares the parking supply with the parking minimums at each policy period, and analyzes the spatial characteristics of parking provision for office use. The descriptive analysis found that the effects of minimum parking requirements (MPRs) on parking provision vary by floor area ratio (FAR), operation period, and transit accessibility. By conducting a geographical weighted regression (GWR) model, this paper further examines the spatially varying effects of the built environment on parking provision. The modeling results conclude that the significance and strength of the effects of built environments on parking provision vary across space. (1) The total parking quantity increases with the growth of the FAR, and this increasing effect is larger in sub",11.0,https://www.mdpi.com/2071-1050/11/4/1142/pdf?version=1550748967
98d2a3ad95444036c7a2fe45df5db141650243cb,10.3390/ijgi11010053,"A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China",2022.0,ISPRS Int. J. Geo Inf.,Hang Shen; Lin Li; Haihong Zhu; Feng Li,"With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighbo",11.0,https://www.mdpi.com/2220-9964/11/1/53/pdf?version=1641971246
fd0bb25b78ce9951274ae030af99c520b2002513,10.3390/buildings15101649,Predictive Modelling for Residential Construction Demands Using ElasticNet Regression,2025.0,Buildings,Elrasheid Elkhidir; Tirth Patel; J. Rotimi,"The residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produce a predictive model for residential construction demand using ElasticNet regression. Adopting New Zealand as a case study and leveraging datasets from Statistics New Zealand, this research identifies key demographic, economic, and market factors influencing four building categories: retirement villages, apartments, multiunit developments, and standalone houses. The research results indicate that age groups, particularly the 20−39 and 65+ age groups, and economic indicators, such as the house price index and unemployment rates, have high prediction powers. The models showed high accuracy for some categories, with R2 values exceeding 0.87 for retirement villages and 0.91 for multi-units. Challenges were encountered with standalone houses and apartments due to residual variance. The research findings highlight the importance of targeted urban planning and policy adjustments to satisfy the",11.0,https://doi.org/10.3390/buildings15101649
d6efab25029442de559badf4b9a344566d81799e,10.1371/journal.pone.0262727,The spatially heterogeneous and double-edged effect of the built environment on commuting distance: Home-based and work-based perspectives,2022.0,PLoS ONE,Zhong Zheng; Su-Hong Zhou; Xing-Dong Deng,"Rich literature has examined the impact of the built environment on commuting distance. Linear models assume that the influence of the built environment is spatially homogeneous. However, given the spatial heterogeneity of urban space, conclusions might be different or even be contrary. The influence of the built environment might also be different by home and work locations. To explore the spatially heterogeneous effect of the built environment from both home-based and work-based perspectives, this study applied large-scale cellular cellphone data in Guangzhou, China. Commuting was measured by decay parameters of probabilistic distributions of commuting distances. Geographically weighted regression models were applied to examine the spatially heterogeneous effect, differentiated by home-based and work-based perspectives. Results confirmed that the impact of the built environment on commuting distance is spatially heterogeneous. The urban space is classified into clusters of central areas, inner suburbs, and outer suburbs. Results also revealed the double-edged effect of the built environment. Residential population, recreation facilities, and mixed development are residence-attrac",11.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0262727&type=printable
72832aa0082b166cd6338d84485b631a57720420,10.3390/su14074344,Is “Attending Nearby School” Near? An Analysis of Travel-to-School Distances of Primary Students in Beijing Using Smart Card Data,2022.0,Sustainability,Cong Liao; Te-Qi Dai,"The distance between home and school is crucial for children’s mobility and education equity. Compared with choice-based enrollment systems, much less attention has been given to the commuting distance to school in proximity-based systems, as if the institutional arrangement of assigning children to nearby schools can avoid the problem of long commuting distances. Using student-type smart card data, this study explored the spatial characteristics of the commuting distance to primary schools by public transport and the residence-school spatial pattern under the proximity-based system in Beijing. The relationships between long school commutes and house price/age were investigated under the context of school gentrification. For the identified primary student users, fewer than 35% of the students travelled fewer than 3 km to school, while more than 80% of students travelled long distances greater than 5 km, which indicated that the policy of “attending nearby school” did not guarantee a shorter commuting distance to school. Long distances to school greater than 5 km correlate negatively with a lower average house price/building age and fewer students. This finding verified the assumpti",11.0,https://www.mdpi.com/2071-1050/14/7/4344/pdf?version=1649241484
46c5f5389679c33aa63a12477f6a550f66921973,10.1371/journal.pone.0190346,World wide spatial capital,2018.0,PLoS ONE,Rijurekha Sen; D. Quercia,"In its most basic form, the spatial capital of a neighborhood entails that most aspects of daily life are located close at hand. Urban planning researchers have widely recognized its importance, not least because it can be transformed in other forms of capital such as economical capital (e.g., house prices, retail sales) and social capital (e.g., neighborhood cohesion). Researchers have already studied spatial capital from official city data. Their work led to important planning decisions, yet it also relied on data that is costly to create and update, and produced metrics that are difficult to compare across cities. By contrast, we propose to measure spatial capital in cheap and standardized ways around the world. Hence the name of our project “World Wide Spatial Capital”. Our measures are cheap as they rely on the most basic information about a city that is currently available on the Web (i.e., which amenities are available and where). They are also standardized because they can be applied in any city in the five continents (as opposed to previous metrics that were mainly applied in USA and UK). We show that, upon these metrics, one could produce insights at the core of the urban",11.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0190346&type=printable
c71a8425e72e4a5bd4b030aad95d79b22ce388c7,10.5901/mjss.2014.v5n7p107,A Multivariate Time Series Analysis of Household Debts during 2007-2009 Financial Crisis in South Africa: A Vector Error Correction Approach,2014.0,,N. Moroke; J. Mukuddem-Petersen; M. A. Petersen,"In this study, we adopted multivariate time series approach to investigate the effect selected macroeconomic variables on household debts using South African data collected from 1990 Q1-2013 Q1. SAS 9.3 was used for the analysis. Johansen multivariate cointegration and the Toda-Yamamoto causality testing approaches were adopted. The ADF and KPSS unit root test results confirmed that all the variables under study were stationary. Cointegration results allowed the acceptance of four cointegrating vectors. The ECM revealed a negative sign implying the existence of a long-run equilibrium relationship among the variables. The coefficient of the error term implied that 13.8% of disequilibrium due to negative and positive shocks was previously corrected in one period per quarter. This adjustment provided the model with all the signs concurring with economic theory and literature on household debts. Toda-Yamamoto causality results revealed a unidirectional relationship running from GDP, UR and ER to HHD. A feedback relationship was discovered between the remaining variables confirming they are not weakly exogenous in South African household debts systems. This study recommends the formulat",11.0,https://doi.org/10.5901/mjss.2014.v5n7p107
57f5efadd7ac8f10d25262f0f8ea3e54caf36161,10.1108/ijhma-01-2022-0008,"(A)symmetric interaction between house prices, stock market and exchange rates using linear and nonlinear approach: the case of Iran",2022.0,International Journal of Housing Markets and Analysis,Davoud Mahmoudinia; Seyed Mohammad Mostolizadeh,"
Purpose
The purpose of this study was to investigate the dynamic interactive link between housing prices, stock market price and effective exchange rate in the Iranian economy for a monthly period from April, 2004, to March, 2019. In addition, for a more accurate analysis, three control and determinates variables including real interest rate, real GDP and FDI have been added to the base model.


Design/methodology/approach
For this purpose, we will consider this issue by developing the study of Lean & Smyth (2014), Ali & Zaman (2017) and Coskun et al (2017) in the framework of ADRL and NARDL models. Also, this study analyzed the asymmetric/non-linear impact of stock market indexes and effective exchange rate on Iran’s housing inflation. Asymmetries imply to both positive and negative changes in the variables.


Findings
The results obtained from the ADRL and NARDL models suggest that the existence of cointegration relationship between housing market price and its determinants. From linear model, we found that the exchange rate and stock market price have a positive effect on the real estate inflation in the short run; this relationship is also confirmed in the long run. Other empi",11.0,
2418a70e23938eedc9f269f59642950297f8cc22,10.3390/ijerph181910210,How Much Are People Willing to Pay for Clean Air? Analyzing Housing Prices in Response to the Smog Free Tower in Xi’an,2021.0,International Journal of Environmental Research and Public Health,Haiyong Zhang; Sanqin Mao; Xin-Yu Wang,"The Smog Free Tower (SFT) in the city of Xi’an, China, is the world’s first outdoor architecture that uses solar energy and filtration technology to purify polluted air. It provides a unique opportunity to explore residents’ willingness to pay for air quality and their related behaviors. Drawing on data collected after the establishment of the SFT, this paper reveals the characteristics of changes in people’s willingness to pay for clean air. We found that, prior to the release of an assessment report on the SFT, housing prices had an inverted U-shaped relationship with the distance to the SFT, which indicated people tended to purchase houses a certain distance away from the SFT. The threshold value of distance was inversely related to the greening ratio of the residential area. However, after the publication of the experimental report on the SFT, housing prices decreased as the distance to the SFT increased, indicating the closer the house was to the SFT, the more likely people were to buy it. These changes confirmed that people are willing to pay for clean air. The convenience of transportation had a significant moderating effect on the willingness to pay for clean air, however. ",11.0,https://www.mdpi.com/1660-4601/18/19/10210/pdf?version=1632983178
b14194a49ebea349cdb50942c8975b34d2e5e19a,10.1080/02513625.2013.827510,Mega-Retail-Led Regeneration and Housing Price,2013.0,,Jae Kwang Lee,"With the ongoing shift in the dynamics of retail development toward inner-city sites, mega-retail-led regeneration schemes have emerged as an important strategy for place marketing in the entrepreneurial city of London. While a number of studies have attempted to examine the effects of these schemes, the focus of previous studies has been limited to their wider economic and environmental effects. However, few studies have empirically investigated their impact on surrounding neighborhoods, particularly deprived neighborhoods. This paper investigates the effects of the Westfield London Shopping Centre on changes in housing price levels in the surrounding neighborhood of White City/Shepherd's Bush, in order to determine whether mega-retail-led regeneration schemes are a key determinant in the process of gentrification. A difference-in-differences analysis was used to assess differentials in the change rates of housing prices between control and treatment groups following the development of Westfield London. It was found that Westfield London development caused an increase in the change rate of housing prices of the treatment group relative to that of the control group. These findings ",11.0,https://www.tandfonline.com/doi/pdf/10.1080/02513625.2013.827510?needAccess=true
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,10.3390/su14063507,Energy Retrofitting for the Modern Heritage Enhancement in Weak Real Estate Markets: The Olivetti Housing Stock in Ivrea,2022.0,Sustainability,Alice Barreca; R. Curto; Giorgia Malavasi; D. Rolando,"The enhancement of Modern Heritage buildings is nowadays a challenging issue as they are often degraded or abandoned and their historical value is not well acknowledged by potential buyers and their owners. Moreover, they are usually energy inefficient and obsolete, but investments for energy retrofit interventions are not always convenient, especially in socio-economic contexts characterized by weak real-estate market dynamics. This paper aims to study the influence of intrinsic and extrinsic characteristics on housing prices and, in particular, to understand whether elements of building cultural connotations or some housing green features are monetized by the real estate market. The UNESCO site “Ivrea, industrial city of the 20th century”, located in the weak real-estate context of the Eporediese territorial area, was selected as an emblematic case study and two data samples of property listings were built to perform spatial regression analyses. The results showed that the green features of housing, such as the heating type and the EPC level, have a greater influence on property prices than those characteristics related to the cultural connotations of a building, such as the Oliv",11.0,https://www.mdpi.com/2071-1050/14/6/3507/pdf?version=1647836103
32c6f0d95f71a0bc0c2b64177de1a0fe5c8f2f86,10.1080/1331677x.2020.1746190,Do house prices squeeze marriages in China?,2020.0,Economic Research-Ekonomska Istraživanja,Chi-Wei Su; Khalid Khan; Lin-Na Hao; R. Tao; Adelina Dumitrescu – Peculea,"Abstract This study reviews the threshold effect of house prices (HPs) on marriage (MR) in China by utilizing a panel threshold regression. The findings indicate that HPs have a positive impact on MR when the price is below the threshold value. Homeownership is an extremely important factor in MR in China, and MR without housing is considered incomplete. However, HPs have a negative effect on MR when the price is higher than the threshold value. Unemployment and female education have a negative effect on MR, while GDP per capita has a positive effect. These results are supported by the duration model, which shows that as HPs increase, the rate of MR decreases. The study makes a contribution on the asymmetric impact of high HPs on MR in China in the two regimes. The paper offers insight into the economic outlook on HPs and MR driven by societal and institutional changes, such as privatization and state ownership of enterprises, that have changed marriage behavior. Increasing HPs slow MR and may have a more serious impact on China than on other countries. The government should balance housing supply and demand by enhancing antimonopoly supervision in the private market. The governmen",11.0,https://www.tandfonline.com/doi/pdf/10.1080/1331677X.2020.1746190?needAccess=true
51fb894f1cc807fdfba811194288da4068d39a71,10.31648/aspal.6111,The Regional Spatial Diversity of Housing Prices and Market Activity – Evidence from Poland,2021.0,Acta Scientiarum Polonorum Administratio Locorum,R. Cellmer; Aneta Cichulska; M. Bełej,"The aim of this study is to identify the factors that significantly determine the regional spatial differentiation of housing prices as well as housing market activity in Poland. The present research makes the assumption that average housing prices and market activity (number of transactions) are regionally shaped by economic, social, infrastructural and environmental conditions which can be described as a set of diagnostic features ascribed to a given area, constituting a statistical unit. Furthermore, it is assumed that individual effects may appear, being tied to the idiosyncrasies and significance of the given area. The time horizon of the research is 2008-2018. Based on data sourced from the Central Statistical Office a panel data was prepared for each of 380 Polish districts (poviats). Next, parameters were estimated for a single-factor panel model, as well as a two-factor model in which the constant term is different for different time periods and different units. This resulted in a model encompassing both average price determinants, and individual effects which reflect certain regularities of their spatial distribution. Moreover, the research will result in a set of cartogr",11.0,https://doi.org/10.31648/aspal.6111
f4836942d465c87b86b7691a878e80bf7c990a1a,10.3389/fpubh.2021.791575,Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai,2021.0,Frontiers in Public Health,Ziliang Lai; Xing-Hua Liu; Wenxiang Li; Ye Li; Guojian Zou; Mei-Ting Tu,"Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m2 to reduce the mean concentration of PM2.5 and NO2 by 1 μg/m3, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help cit",11.0,https://www.frontiersin.org/articles/10.3389/fpubh.2021.791575/pdf
d9aa8e7e33cedc2567dc8a94bbf2b485d5057b2d,10.1108/ijhma-10-2023-0139,Determinants of housing prices: evidence from East Coast Malaysia,2024.0,International Journal of Housing Markets and Analysis,Siti Hafsah Zulkarnain; Abdol Samad Nawi; M. A. Esquivias; Anuar Husin,"
Purpose
The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment.


Design/methodology/approach
The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index.


Findings
This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant.


Originality/value
Malaysi",11.0,
115e8948552c5e6ac301a4106491fe2db5285e79,10.4314/wsa.v38i1.16,Swimming pools and intra-city climates: influences on residential water consumption in Cape Town.,2012.0,,Raymond Siebrits,"Water demand management can be effective as a resource management approach if demand estimation is accurate and consumption determinants are defined. While determinants such as household income, regional climate, water price, property size and household occupancy have been comprehensively studied and modelled, other determinants such as swimming pools and intra-city climates have not. This study examines residential water consumption in the City of Cape Town in 2008/2009, under property size regimes, to separately determine whether the presence of pools or occurrence of different intra-city precipitation patterns have an influence on water consumption. A sample of 14 233 properties is analysed, with 20.86% having swimming pools within their boundaries. Overall, those properties with swimming pools used 37.36% or 8.85 kl per month more water than those without, with pools having a larger influence on household consumption on smaller properties. These results were statistically significant. Different precipitation patterns occurred over the study period, and while there were indications that consumption may be lower if there is more rainfall, limited evidence was found to support the",11.0,https://www.ajol.info/index.php/wsa/article/download/73319/62249
7a584d1702a14082a036332e3e21aaecc3480d4e,10.3390/economies8030059,Internal Migration in Spain: A Complementary Approach,2020.0,Economies,A. Maza,"This study examines the determinants of internal migration of working-age population among provinces in Spain in the aftermath of the economic crisis. It pays special attention to two features of migration that have not been sufficiently studied so far: (a) Distance, namely the differences between the determinants that are behind short-distance and long-distance movements; (b) Age, that is to say, whether young and adult migrants hold different motivations. To accomplish this aim, an extended model is used in which, apart from a variable capturing distance, both economic and non-economic (amenities) variables are included. Findings reveal that both economic factors (mainly unemployment and income) and amenities (measured by a climate condition variable) are more important for adult population and long-distance movements than for young population and movements between neighbouring provinces. Additional findings seem to convey the message that, as far as housing prices are concerned, they play a significant role when migration takes place between neighbouring provinces, but lose importance when all migrations are studied together. Moreover, the paper’s results do not cast, as expecte",11.0,https://www.mdpi.com/2227-7099/8/3/59/pdf?version=1595496581
6c35cd1594dd5b08498d26ff1c10333588fdda99,10.1177/21582440221097926,Revisiting Hukou Transfer Intentions Among Floating Population in Chinese Cities: Spatial Differences and Multi-Level Determinants,2022.0,SAGE Open,Xin Lao; Zhihao Zhao; Heng-Yu Gu,"The hukou transfer intentions (HTI), as an essential indicator of migration behaviors, is of great significance to the urbanization development and the citizenization process of China’s floating migrants. Although extant studies have devoted extensively to the influence factors of HTI, their spatial differences and multi-level determinants are comparatively under-researched. Based on the China Migrants Dynamic Survey (CMDS) data in 2016, we employed the spatial analysis method to discover a remarkable spatial disparity of HTI among cities of different sizes and found that HTI is affected by individual-level and city-level factors using a multilevel model. Besides city sizes and economic development levels, city amenity factors and housing prices also positively impact HTI. The cross-level interactions also indicate the greater preference of older, higher-educated, and wealthier floating migrants for larger cities than their counterparts, and the greater preference of older, higher-educated, and longer-term floating migrants for cities with higher housing prices than their counterparts, implying an accelerated agglomeration of talent with more social capital in leading cities. Sever",11.0,https://doi.org/10.1177/21582440221097926
b4b75145918883428161627464c16a37322fe550,10.2478/v10089-012-0020-6,Recent dynamics in the Portuguese housing market as compared with the European Union,2012.0,,Fátima Loureiro de Matos,"Abstract. The closing years of the 20th century witnessed profound changes in the European housing market, characterised by an increase in owner-occupied housing; instability of house prices (leading to a serious crisis in some regions, in light of a fall in demand); a change in people’s attitude to the housing market, involving a rise in the importance of location, the quality of materials and spaces, environmental sustainability, and architectural and urban innovation. The European Union does not have a common housing policy, which it believes is the responsibility of Member-States. However, it is recognised that the problems related to the socio-urban inclusion of low-income individuals do have an impact on Community policies. Because we are in the midst of a transformation, it is difficult to talk today about the dynamics and policies of housing, the functioning of markets, and thereby about the ways in which public administrations are facing the current crisis of real estate overproduction. This article aims at analysing the dynamics of the housing market in Portugal as compared with other European countries, highlighting the characteristics of demand and supply and the main c",11.0,https://apcz.umk.pl/BGSS/article/download/v10089-012-0020-6/10.2478%2Fv10089-012-0020-6
d8b7e0af98ec6441abd51af97f512015ea6ae69f,10.32996/jbms.2024.6.5.9,Explainable AI and Machine Learning Model for California House Price Predictions: Intelligent Model for Homebuyers and Policymakers,2024.0,Journal of business and management studies,Azam Khan; Pravakar Debnath; Abdullah Al Sayeed; Md Fakhrul Islam Sumon; Arifur Rahman; Tushar Khan; Laxmi Pant,"California's housing and real estate market is one of the most valuable markets in the USA. Many shareholders such as individual homebuyers, home sellers, real estate agents, lenders, and policymakers depend on high-volume information regarding the dynamics at work and their correct estimation. The research project aimed at developing an Explainable AI machine-learning model for California house price predictions. Data on house prices were collected from reliable sources such as California home estate websites, land sites, and public datasets. Features of the data included location, size, number of rooms, area type, availability, sale prices, and oceanic proximity. In this research project, credible, proven, and renowned machine learning algorithms were used most notably, Linear Regression analysis, XG-Boost, and Random Forest. The Random Forest came up quite impressively with a superior accuracy score and low MAE and MSE; thus, it was good for learning the underlying best patterns and relationships that may exist within the data for house price predictions. XG-Boost also did relatively well, showcasing moderately high accuracy and relatively low MSE and MAE, compared to the Linear",11.0,https://al-kindipublisher.com/index.php/jbms/article/download/7929/6651
e327918a4a468e9a3ba94b6a5032bf9d5d08f1da,10.1177/00420980241298199,Upzoning and gentrification: Heterogeneous impacts of neighbourhood-level upzoning in New York City,2024.0,Urban studies,Minjee Kim; Hyojung Lee,"In light of the calls to relax restrictive density regulations, this paper examines how increasing residential development capacity, i.e. upzoning, may change the demographic, socio-economic and housing characteristics of the affected neighbourhoods. We examine the neighbourhood-level upzonings of New York City to answer this question. We find that upzoning is positively associated with signs of gentrification – upzoned neighbourhoods became whiter, more educated and more affluent in the long run. Upzoning is also associated with increases in housing production, but housing prices also increased. Most importantly, we find that these effects varied significantly by the intensity of upzoning and the pre-upzoning local contexts. Neighbourhoods affected by intense upzonings experienced gentrification more intensely, along with greater housing production, rent growth and housing price appreciation. Black-majority and low-income neighbourhoods experienced gentrification to the greatest extent, while neighbourhoods with high demand for housing saw the greatest increases in housing supply. We discuss different mechanisms of gentrification likely at play for the different types of neighbour",11.0,https://doi.org/10.1177/00420980241298199
bfd963fc28c837cd06c9679f47649a4b6f894553,10.3808/jei.202300503,How Landscape Patterns Affect River Water Quality Spatially and Temporally: A Multiscale Geographically Weighted Regression Approach,2023.0,Journal of Environmental Informatics,X. Li; J. Zhang; W. Yu; L. Liu; W. Wang; Z. Cui; R. Wang; Y. Li,"The water quality of a river can be considered a function of its surrounding landscape. Understanding the relationship between landscape patterns and river water quality is essential for optimizing landscape patterns to reduce watershed pollution and has not yet been solved. A multiscale geographically weighted regression (MGWR) model was used to explore the associations between the landscape patterns and water quality. Our results showed that landscape metrics have varied relationships with the water quality across spatial scales in different seasons. The strongest independent influencing variable for NO3–-N, NH4+-N, and TN was tea gardens, residential land, and varied seasonally, respectively. The impacts of the landscape metrics on the TP were relatively weak throughout the year at the watershed scale. The influence of landscape metrics on NO3–-N was more significant during the flood season, whereas that on NH4+-N was more notable during the non-flood season. Seasonal changes in the influencing landscape metrics of TN were not regular. Although landscape composition more significantly influenced water quality than configuration, the Shannon’s diversity index and patch density we",11.0,
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,10.2478/remav-2023-0007,Points of Interest and Housing Prices,2023.0,Real Estate Management and Valuation,R. Cellmer,"ABSTRACT Points of Interest (POI) are an inherent element of the urban landscape, and their number and density reflect, among other things, the degree of urbanization and the city’s spatial structure. The very presence of POI in the closest vicinity of a residential property may indirectly or directly affect housing value. This paper aims to demonstrate the usefulness of POI density information of different categories in assessing the quality of a property’s immediate surroundings. While the mere presence of POIs in the nearest neighborhood may affect real estate prices, the influence of specific categories may not necessarily be positive. Therefore, the study attempted to classify POIs and determine their importance in the price formation process using spatial regression models. The results indicate that a high density of POIs in the immediate area is a stimulant for housing prices. The detailed analysis indicated that only some POI categories might be related to transaction prices, while in certain situations, some POI categories may negatively impact prices.",11.0,https://sciendo.com/pdf/10.2478/remav-2023-0007
f20055c3edaa910192a90258f6367e59ecb3410d,10.3390/ijgi10060359,Examining the Effect of Squatter Settlements in the Evolution of Spatial Fragmentation in the Housing Market of the City of Buenos Aires by Using Geographical Weighted Regression,2021.0,ISPRS Int. J. Geo Inf.,A. F. Ogas-Mendez; Y. Isoda,"The spatial fragmentation in the housing market and the growth of squatter settlements are characteristic for the metropolitan areas in developing countries. Over the years, in large cities, these phenomena have been promoting an increase in the spatial concentration of poverty. Therefore, this study examined the relationship between the squatter settlement growth and spatial fragmentation in the housing market of Buenos Aires. By performing a spatiotemporal analysis using geographically weighted regression in the house prices for the years 2001, 2010, and 2018, the results showed that while squatter settlements had a strong negative effect on house prices, the affected areas shifted over time. Our findings indicate that it is not the growth of the squatter settlement that causes spatial fragmentation, but rather the widening income disparities and further segregation of low-income households. However, squatter settlements determined the spatial demarcation of fragmented housing market by attracting low-income households to surrounding low house price areas.",11.0,https://www.mdpi.com/2220-9964/10/6/359/pdf?version=1621926111
65c101667901ca980e98b3383129fd23f2e134fc,10.14716/ijtech.v10i6.3640,Land Value Capture Modeling in Commercial and Office Areas using a Big Data Approach,2019.0,,M. Berawi; N. Suwartha; Fathiya Salsabila Fathiya Salsabila; G. Gunawan; Perdana Miraj Perdana Miraj; R. Woodhead,"Infrastructure development in Indonesia creates massive impacts on the economy. The Light rail transit (LRT) of greater Jakarta (Jabodebek) project has been estimated to have cost more than 29 trillion rupiahs due to land acquisition and route planning. The urban transit development may impact to the price of property including residential, commercials and offices along the route. This research aims to determine variables affecting the price elasticity of property and the correlation to station proximity. Data mining through web scrapping was used to assess the degree of correlation between price elasticity and station location. The result shows that approximately 13% of the commercial property was spread over a distance of 1 km from the LRT station. The closer a property to transit station, the price will be twice cheaper compared to those located further. The findings also show variables that highly contribute to property prices including schools, hospitals, and proximity to some of transit stations located in city center of Jakarta and building density.",11.0,https://ijtech.eng.ui.ac.id/download/article/3640
f421f827c9a2a7b5f67ab386e5dc51233bba246e,10.2139/ssrn.3525995,Wealth Effect on Consumption during the Sovereign Debt Crisis: Households Heterogeneity in the Euro Area,2020.0,Social Science Research Network,Bertrand Garbinti; P. Lamarche; Charlélie Lecanu; F. Savignac,"This paper studies the heterogeneity of the marginal propensity to consume out of wealth (MPC) both across and within countries. We estimate the MPC based on a cross-country harmonized household level dataset which combines surveys on wealth, income and consumption. We use panel regressions and an instrumental variable approach. First, our panel-based MPC estimates are very similar to those obtained on aggregate data and show substantial heterogeneity across countries. The wealth effect is coming both from housing and financial assets, while the main asset channel varies between countries. Second, the MPC is higher for low-wealth households, whatever the country. Third, we find some asymmetries across countries regarding the reaction to losses versus gains. Fourth, higher MPC is obtained for the two main consumption expenditure categories. Fifth, we find evidences that housing prices shock decreases consumption inequality while financial wealth shocks have a limited effect on consumption inequality. Classification-JEL: D12, E21, C21",11.0,https://publications.banque-france.fr/sites/default/files/medias/documents/wp751.pdf
a25a291536324283e22dd55230ecdbc90b664552,10.3390/su11020544,Spatial Spillover of House Prices: An Empirical Study of the Yangtze Delta Urban Agglomeration in China,2019.0,Sustainability,Ling Zhang; He Wang; Yan Song; Hai-Zhen Wen,"This study investigates the spatial dependence of house prices in the Yangtze Delta Urban Agglomeration since the year 2000. According to Moran’s I index and the LISA scatter plot derived from a cross-section data set, the spatial dependence of house prices can be traced across the 25 cities in the agglomeration and became more evident after 2005. This study develops a spatial panel model with geographical distance and economic distance weight matrices. Spatial effects significantly influenced house prices in both cases but the intensity of the former was weaker than for the latter. Income, proportion of the tertiary industry, and amenity exhibited significant indirect effects on house prices in other cities in the inner region of the agglomeration, while competition of population between cities with economic proximity exerted negative indirect effects. Furthermore, urban industrial structure, innovation capability, and urbanization degree revealed differences in terms of spatial dependence among various city groups.",11.0,https://www.mdpi.com/2071-1050/11/2/544/pdf?version=1548063732
aefca762e7159d4693f86d8e570d75e93f953f00,10.1016/j.jeem.2020.102417,Measuring environmental (dis)amenity value during a pandemic: Early evidence from Maryland,2021.0,Journal of Environmental Economics and Management,Nicholas B. Irwin; Mitchell R. Livy,"With the outbreak of COVID-19 and the implementation of stay-at-home (SAH) orders aimed to mitigate its spread, households became less mobile and sheltered in place. This behavior has potential implications for how households’ value environmental (dis)amenities, especially those that are underutilized during the pandemic. In this paper, we explore changes in the valuation of two prominent environmental (dis)amenities – major roadway and open space proximity – by households within the Baltimore metropolitan region. We find evidence that the housing price capitalization of immediate major roadway proximity changes due to the SAH order and associated policies that impact economic activity, suggesting a shift in household perceptions, while there is no evidence of open space valuation changes. These results may have significant implications for homeowner welfare if the altered capitalization of environmental (dis)amenities is temporary due to the SAH order.",11.0,https://www.sciencedirect.com/science/article/pii/S0095069620301406/pdf
8e01b20eeb2c93251114956a2a7b245873d34eb2,10.14254/2071-789x.2020/13-1/3,Demand for residential mortgage loans and house prices in the euro area,2020.0,,Silvo Dajčman,"DOI: 10.14254/2071789X.2020/13-1/3 ABSTRACT. This paper explores the determinants of demand for residential mortgage loans in the euro area, with a focus on the house prices–demand for loans nexus. We firstly identify the demand for residential mortgage loans and then empirically assess a model for an unbalanced panel of 13 euro area countries, covering the period 2003Q1–2016Q3. Our results show that the growth of house prices is positively associated with changes in demand for residential mortgage loans. A positive association was also found between GDP growth and changes in demand for mortgage loans. Growth in residential mortgage interest rates and changes in bank credit standards for residential mortgage loans are negatively associated with changes in demand for residential mortgage loans. We found that these associations generally became more significant after the start of the subprime mortgage crisis in the U.S. rather than before the crisis.",11.0,https://www.economics-sociology.eu/files/3E_729_Dajcman.pdf
ee753ad5f58fb9292314e008dccb855d182f289c,10.5089/9798400227042.001,Don’t Look Up: House Prices in Emerging Europe,2022.0,IMF Working Papers,Serhan Cevik; Sadhna Naik,"Abstract This paper investigates how housing prices respond to economic, financial and demographic conditions in emerging markets in Europe. We use quarterly data covering 10 countries over the period 1998–2022 and implement a panel quantile regression approach to obtain a granular analysis of real estate markets. Overall, economic, financial and demographic developments explain real house price growth in emerging Europe, with income growth having the most significant impact. Quantile regression estimations show that income growth matters more for higher quantiles of the property market. We also find that an increase in short-term or long-term interest rates has a price-dampening impact, indicating that a higher cost of borrowing is associated with lower real house price growth. These results indicate that the downturn in house prices could deepen with the looming economic recession and soaring interest rates.",11.0,https://www.elibrary.imf.org/downloadpdf/journals/001/2022/236/001.2022.issue-236-en.xml
556ed929f42725e3cfee065d7013ded790b2bc60,10.1145/3449726.3463141,House price prediction using clustering and genetic programming along with conducting a comparative study,2021.0,GECCO Companion,Fateme Azimlu; S. Rahnamayan; Masoud Makrehchi,"One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enh",11.0,
6f0e952fb55a010a0ffdc91e3472469a178aec38,10.1109/icscss57650.2023.10169290,House and Rent Price Prediction System using Regression,2023.0,2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS),Sujata Khandaskar; Chirag Panjwani; V. Patil; Deanna Fernandes; Prerna Bajaj,"Real estate market analysis plays a crucial role in predicting house prices and rental trends. In recent years, several techniques have been developed to address this challenge, including machine learning (ML) algorithms, data mining approaches, and statistical models. There were a few issues which needed to be solved, like data quality and availability pose significant obstacles. Housing datasets often exhibit missing values, outliers, and inconsistent formats, which can hinder the performance of prediction models. Addressing these challenges requires careful data preprocessing techniques and collaboration with data providers and government agencies to ensure data accuracy and coverage. Existing systems face significant challenges that hinder their accuracy and reliability. Some of these challenges include limited availability of comprehensive and up-to-date data, complex market dynamics, and the need for efficient feature selection and model optimization. The proposed objectives includes Data collection and preprocessing, feature selection and engineering, Model selection and development along with evaluation, usability, comparison with existing systems, etc. By addressing these ",11.0,
d93f431e0143081ce1b5acb2d51816c030ba640e,10.1108/fs-04-2025-0083,Forecasts of residential property price indices for Hong Kong through machine learning,2026.0,Foresight,Bing-Zi Jin; Xiao-Jie Xu,"
 
 Up to the current fall patterns that started at the end of 2021, the real estate market in Hong Kong has developed at such a rapid pace throughout the course of the previous few decades. Therefore, anticipating future property values has become a huge difficulty for both the government and investors. This is because of the current economic climate. The purpose of this study is to investigate the real estate price forecasting problem for the Hong Kong market.
 
 
 
 Within the scope of this study, the authors use Gaussian process regressions with a range of kernels and basis functions to explore quarterly residential property price indices for Hong Kong from the fourth quarter of 1979 to the first quarter of 2024. The authors make use of cross-validation and Bayesian optimizations to train models and carry out forecasting exercises using estimated models.
 
 
 
 Out of sample, the models that were developed were able to accurately predict the price indices from the second quarter of 2015 to the first quarter of 2024. The relative root mean square error for these models was 0.3080%.
 
 
 
 It is possible that these results might be used either on their own or in conjunction with ",11.0,
6c5680d0a52b127efa0566150cd697a1f534f855,10.1108/ijhma-10-2018-0077,Determinants of house price volatility in Namibia,2019.0,International Journal of Housing Markets and Analysis,T. Kaulihowa; K. Kamati,"
Purpose
This paper aims to test the volatility and analyses the macroeconomic determinants of house price volatility in Namibia over the period 2007 Quarter 1 to 2017 Quarter 2. It further explores the causal relations between house price volatility and its determinants.


Design/methodology/approach
The study used autoregressive conditional heteroskedastic and generalized autoregressive conditional heteroskedastic models to test for volatility. The vector error correction model was used to analyse the determinants and causal relations.


Findings
The results support the hypothesis that house prices in Namibia exhibits persistent volatility. It was further established that past period volatility’ GDP and mortgage loans are the key determinants of house price volatility. Additionally’ there exists unidirectional causality from GDP and mortgage loans to house price volatility.


Practical implications
Policy implications emanating from the study implies that macroeconomic fundamentals should be monitored closely to mitigate the issues of house price volatility.


Originality/value
The study is the first of its kind in Namibia to address the pertinent issues of ever increasing housin",11.0,
7b99cbcd11d333f022fa6072427c8a7b5b0305a6,10.3727/108354219x15652651367460,Towards Understanding the Volatility of Housing Prices and Exploring the Tourism Demand Impact,2019.0,Tourism Analysis,H. O. Balli; Faruk Balli; Susan Flint-Hartle; Xinping Yang,"Using an Exponential Generalized Autoregressive Conditional Heteroskedasticity model (EGARCH), the volatile nature of residential property prices in New Zealand is examined. The model investigates the extent to which macroeconomic fundamentals and a combined population shift/visitor
 factor called ""tourism"" impact residential property values and capture variations in volatility across different regions. We find that fundamentals have different impacts across regional property markets. In line with the literature, the bigger metropolitan regions like Auckland and Christchurch
 are more sensitive to macroeconomic factors than smaller regions. Additionally, we find house price volatility in southern regions is affected by macroeconomic fluctuations more than northern areas. Novel to the literature, we discover that ""tourism"" has significant impact on house price volatility
 as well, particularly in popular touristic areas. These results imply that to better understand the determinants of house price volatility and mitigate its effects, policy makers should consider a coordinated approach, taking a regional perspective and the interaction of several variables
 into account.",11.0,
f8e4174661fd5bca39fba8b0076fb8faad7c42e5,10.1142/s0129054122420163,"Research on Prediction of Housing Prices Based on GA-PSO-BP Neural Network Model: Evidence from Chongqing, China",2022.0,International Journal of Foundations of Computer Science,Ziyi Sun; Jing Zhang,"Since 2000, the real estate industry has experienced rapid development, and at the same time, it has driven the rapid growth of housing prices, and the trend of housing prices has attracted attention. This paper integrates genetic algorithm and particle swarm algorithm to optimize BP neural network, and establishes a housing price prediction model based on mixed genetic particle swarm BP neural network. The average data of housing prices in Chongqing, China from 2000 to 2020 and several main factors affecting the trend of housing prices were selected as experimental data. Through the training and simulation prediction based on the mixed particle swarm BP neural network, the error between the predicted value and the actual value was within 0.5%, the validity and accuracy of the model are proved. At the same time, this paper predicts the average price of residential commercial housing in Chongqing in 2021, which provides a reference for the government’s macro-control and sellers to carry out residential commercial housing.",11.0,
e5ec9681077794dd71b0e2c0b9222f65d4da1649,10.1080/00330124.2022.2111692,"Exploring Housing Determinants of Obesity Prevalence Using Multiscale Geographically Weighted Regression in Chicago, Illinois",2022.0,Professional Geographer,A. Lotfata; Mateusz Tomal,"To date, research has largely ignored residential determinants of obesity prevalence. There is very little information on whether these impacts change spatially and operate at different spatial scales. As a result, the purpose of this study is to identify the housing determinants of obesity in Chicago, Illinois, using multiscale geographically weighted regression. The main factors associated with obesity prevalence in Chicago, according to the findings, are housing affordability and housing stability. The rising burden of housing rent expenses, in particular, is related to an increase in the percentage of obese people. Additionally, the study found that the relationships between housing covariates and obesity are spatially heterogeneous and operate at various spatial scales. Finally, this study calls for multiscale and multidisciplinary policies to reduce obesity prevalence.",11.0,
bf7b9f70936d1d04fcdc6c023f0aafd41d3912db,10.1177/13548166221097585,How to better incorporate geographic variation in Airbnb price modeling?,2022.0,Tourism Economics,Yifei Jiang; Hong-Lei Zhang; Xian-Ting Cao; Ge Wei; Yang Yang,"Since entering the Chinese market in 2015, Airbnb has become a major player in the Chinese home-sharing arena. This article uses data from 8012 active Airbnb listings in Shanghai and presents three models (linear regression, geographically weighted regression, and random forest) to study the determinants of Airbnb listing prices and incorporate geographic variation in price modeling. Results show that property quality plays a key role in shaping listing prices. Due to Airbnb’s distinctions from traditional lodging in both features and business models, Airbnb pricing determinants differ accordingly. For example, location conditions were found to have a limited impact in regions with established transportation networks. Among the three models, random forest performed best in terms of prediction accuracy. Lastly, practical implications are discussed.",11.0,
2c7ce8f631db5c5633cb30a2b786381dd35f7c2a,10.1177/1091142114537675,Tax Base Erosion and Inequity from Michigan’s Assessment Growth Limit,2015.0,Social Science Research Network,Timothy R. Hodge; M. Skidmore; G. Sands; Daniel P. McMillen,"We examine the degree to which Michigan’s property value assessment growth cap has eroded the tax base and created substantial differences in effective tax rates among residential properties within the City of Detroit. While the analysis focuses on a specific city with significant tax base erosion challenges, it is relevant to other cities in Michigan and across the nation, particularly in states that impose assessment growth limits. Using quantile regression techniques, we examine how an assessment growth cap alters effective tax rate distributions within and across property value groups. Results show that the cap creates a wide range of effective tax rates across properties of similar value (horizontal inequity) and similar tax payments for properties of differing values (vertical inequity).",11.0,
dfd18374bf96eb3724e9ab3258fd7d632d3faac7,10.1111/meca.12253,House prices and immovable property tax: Evidence from OECD countries,2019.0,Metroeconomica,Tommaso Oliviero; Agnese Sacchi; A. Scognamiglio; A. Zazzaro,"In this paper we study the impact of changes in immovable property taxation on the growth rate of house prices by analyzing a panel of 34 OECD countries over the period 1970-2014. We show that there is a negative relationship, robust to the inclusion of other cyclical determinants of house prices, country and year fixed effects. Furthermore, we do not find evidence of a stabilizing role of immovable property taxes on the variability of house prices: boom-bust cycles in housing markets are, in fact, not correlated with the levels of such a tax.",11.0,
5faee46c0833928b29cfb6aade41ed28d4527589,10.1016/j.econmod.2023.106252,A new framework to disentangle the impact of changes in dwelling characteristics on house price indices,2023.0,Economic Modelling,P. Reusens; F. Vastmans; S. Damen,,11.0,
b776d1c4bf5a8fe5b2ccd5a6ccf76e555ecdc49d,10.1080/10293523.2005.11082467,The determinants of residential house prices in South Africa,2005.0,,B. Standish; B. Lowther; R. Morgan-Grenville; C. Quick,,11.0,
7955ccf99ff8661d0cd58598160435f5bc3ffa1a,10.1080/12265934.2016.1273129,The varying impact of land subsidence and earth fissures on residential property values in Maricopa County – a quantile regression approach,2017.0,,James J. Yoo; Thomas V. Frederick,,11.0,
ee5819b504637f53b33d66f1d83894d0524f96d5,10.1080/08965803.2021.1925498,"House Prices, Open Space, and Household Characteristics",2021.0,,Tracy M. Turner; Youngme Seo,,11.0,
382d09edfd87b2051617bd758166c9ccef2d0019,10.1016/j.apgeog.2024.103486,Capturing the distance decay effect of amenities on housing price using explainable artificial intelligence,2025.0,Applied Geography,Hojun Lee; Hoon Han; Christopher J. Pettit,,11.0,
5f2b534b8c71c8bc79294c99626fc704155b02ea,10.1101/2020.05.01.20087403,Who is more susceptible to Covid-19 infection and mortality in the States?,2020.0,medRxiv,Yang Han; Victor O. K. Li; J. C. Lam; Peiyang Guo; Ruiqiao Bai; Wilton W. T. Fok,"Background: A novel coronavirus was detected in Wuhan, China and reported to WHO on 31 December 2019. WHO declared a global pandemic on 11 March 2020. The first case in the US was reported in January 2020. Since mid-March 2020, the number of confirmed cases has increased exponentially in the States, with 1.1 million confirmed cases, and 57.4 thousand deaths as of 30 April 2020. Even though some believe that this new lethal coronavirus does not show any partiality to the rich, previous epidemiological studies find that the poor in the US are more susceptible to the epidemics due to their limited access to preventive measures and crowded living conditions. In this study, we postulate that the rich is more susceptible to Covid-19 infection during the early stage before social distancing measures have been introduced. This may be attributed to the higher mobility (both inter- and intra-city), given their higher tendency to travel for business/education, and to more social interactions. However, we postulate after the lockdown/social distancing has been imposed, the infection among the rich may be reduced due to better living conditions. Further, the rich may be able to afford better me",10.0,https://www.medrxiv.org/content/medrxiv/early/2020/05/06/2020.05.01.20087403.full.pdf
7ab47b63742262d657bbdcab5cef5bd49b89363f,10.3390/su11020471,"The Effect of HOPSCA on Residential Property Values: Exploratory Findings from Wuhan, China",2019.0,Sustainability,Zhijiao Qin; Yan Yu; Dian-Feng Liu,"Mixed-use development is theoretically considered to enhance the residential value of an area. However, limited empirical research has focused on European and North American cities. HOPSCA is a real estate project that integrates hotels, offices, parks, shopping malls, conference centers, and apartments. As an important mixed-use development project in Chinese cities, HOPSCA is designed to improve the quality of urban life and to enhance the residential value of an area. Few studies have explicitly examined the effect of HOPSCA on residential property values, let alone linked this question to particular types of HOPSCA. To bridge this research gap, we selected Wuhan City in China as a case study to explore the effects of HOPSCA on residential property values. Specifically, we used the potential model to quantify the effects of HOPSCA and used the geographically weighted regression (GWR) method to estimate the relationship between HOPSCA variables and residential property values. The results are as follows: (1) the effects of HOPSCA on residential property values are statistically significant with positive and negative effects. The balanced-development HOPSCA generated the greatest ",10.0,https://www.mdpi.com/2071-1050/11/2/471/pdf?version=1547712096
f2c7836df75238f5583aeb06d704ed029c7069dc,10.1371/journal.pone.0321951,Real estate valuation with multi-source image fusion and enhanced machine learning pipeline,2025.0,PLoS ONE,Lin Deng,"The automated valuation model (AVM) has been widely used by real estate stakeholders to provide accurate property value estimations automatically. Traditional valuation models are subjective and inaccurate, and previous studies have shown that machine learning (ML) approaches perform better in real estate valuation. These valuation models are based on structured tabular data, and few consider integrating multi-source unstructured data such as images. Most previous studies use fixed feature space for model training without considering the model performance variation brought by various feature configuration parameters. To fill these gaps, this study uses Hong Kong as a case study and proposes an enhanced ML-based real estate valuation framework with feature configuration and multi-source image data fusion, including exterior housing photos, street view and remote sensing images. Eight ML regressors, namely, Random Forest, Extra Tree, XGBoost, Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear Regression (MLR) are used to formulate ML pipelines for training. The SHapley Additive exPla",10.0,https://doi.org/10.1371/journal.pone.0321951
a9af6f8c6d66b7ce16caabc06a456a45ffad110f,10.17645/up.v8i1.6015,“Passive” Ecological Gentrification Triggered by the Covid-19 Pandemic,2022.0,Urban Planning,Dani Broitman,"Urban areas can be conceptualized as large and ever-changing playgrounds in which many diverse agents (households, businesses, developers, municipalities, etc.) are active. The interactions between the playground qualities and the players’ preferences are not unidirectional. However, sometimes, external events may change the perception of the playground qualities in the player’s eyes. The recent Covid-19 pandemic and its associated precautionary measures are a clear example. During the pandemic, the value of existing urban green infrastructures has increased, as lockdowns were imposed, and distance working became widespread. The concept of “passive” ecological gentrification is developed in order to characterize this type of process. In contrast with “active” ecological gentrification, caused by purposeful intervention in the urban arena, “passive” ecological gentrification is triggered by a change of context, such as the pandemic impacts. This article focuses on the appreciation of green urban infrastructures by urbanites during the pandemic, showing that the willingness to pay to live near green and open spaces has increased in general, but with significant spatial differences. T",10.0,https://www.cogitatiopress.com/urbanplanning/article/download/6015/6015
2cc7c0d44d73f113c0ff7c290cee59ce8d81e5b7,10.1136/jech-2016-207249,How do individual-level sociodemographics and neighbourhood-level characteristics influence residential location behaviour in the context of the food and built environment? Findings from 25 years of follow-up in the CARDIA Study,2016.0,Journal of Epidemiology and Community Health,Pasquale E. Rummo; D. Guilkey; J. Shikany; J. Reis; P. Gordon-Larsen,"Background Little is known about how diet-related and activity-related amenities relate to residential location behaviour. Understanding these relationships is essential for addressing residential self-selection bias. Methods Using 25 years (6 examinations) of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study (n=11 013 observations) and linked neighbourhood-level data from the 4 CARDIA baseline cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California, USA), we characterised participants’ neighbourhoods as having low, average or high road connectivity and amenities using non-hierarchical cluster analysis. We then used repeated measures multinomial logistic regression with random effects to examine the associations between individual-level sociodemographics and neighbourhood-level characteristics with residential neighbourhood types over the 25-year period, and whether these associations differed by individual-level income. Results Being female was positively associated with living in neighbourhoods with low (vs high) road connectivity and activity-related and diet-related amenities among high-income individuals only. At ",10.0,https://jech.bmj.com/content/jech/71/3/261.full.pdf
777a06b6a772b70153b2268908089e7e39ce5250,10.5267/j.dsl.2022.6.002,Determining the factors influencing residential property price: A comparative study between Indonesia and Malaysia,2022.0,Decision Science Letters,R. A. Rahadi; S. K. Wiryono; Y. Nainggolan; K. F. Afgani; R. Yaman; A. Azmi; F. Ismail; J. Saputra; D. Rahmawati; A. Moulyni,"The property is a unique product that cannot be contrasted with other commercial products due to pricing conditions. Property price determination is one of the crucial aspects of property development activities because of the profit margin made by the developer and the purchasing preferences. This study attempts to extend the literature that has largely focused on factors of housing prices in developed markets and provided recent evidence of housing price determinants in two countries (i.e., Indonesia and Malaysia). Thus, this study examines the factors affecting housing prices in Jakarta Metropolitan Region and Greater Kuala Lumpur. A quantitative approach was used involving two countries, namely Indonesia and Malaysia. The data was collected using a survey questionnaire through purposive sampling. A total of 100 respondents (Indonesia) and 134 respondents (Malaysia) participated in this study. The data was analyzed using descriptive (frequency) and inferential statistics (chi-square test and multinomial regression). The results indicated that housing location, property funding, and health have a significant effect on residential property prices in Indonesia. Besides that, the res",10.0,https://doi.org/10.5267/j.dsl.2022.6.002
f6a5716832ec2d2bb53084b148f61dd7deea839f,10.32996/fcsai.2023.2.2.5,Beyond FICO: Enhancing Mortgage Default Forecasting and Inclusive Lending via Multimodal Graph Neural Networks and Urban Mobility Analytics,2023.0,Frontiers in Computer Science and Artificial Intelligence,Imran Hossain Rasel; M. Ibrahim; Anika Anjum Pritty; A. M. Fahim; Nusrat Jahan,"Mortgage underwriting in the United States still relies heavily on FICO-style bureau scores, debt-to-income ratios, and loan-to-value cutoffs, even though default emerges from a broader system of household liquidity, neighborhood shocks, housing-market conditions, and lender relationships. This paper develops a framework for improving mortgage default forecasting and inclusive lending through multimodal graph neural networks (GNNs) enriched with urban mobility analytics. Drawing on evidence from mortgage finance, credit scoring, housing economics, network science, and mobility research, the study argues that FICO-centered pipelines understate both relational risk and place-based resilience. Borrowers with similar scores can face different default hazards when commuting stability, job accessibility, local price volatility, and origination-channel exposures differ. The paper synthesizes relevant sources and proposes an empirical design combining Freddie Mac loan-level performance data, HMDA disclosures, FHFA house-price indices, American Community Survey neighborhood variables, and Census LEHD commuting-flow measures. A heterogeneous graph architecture connects borrowers, loans, prop",10.0,https://alkindipublisher.com/index.php/fcsai/article/download/12306/10988
a4b27e383338ce63f68fcad557659d9a9d7a6e6d,10.3390/ijerph20054050,"Urban Tree Canopy and Environmental Justice: Examining the Distributional Equity of Urban Tree Canopy in Guangzhou, China",2023.0,International Journal of Environmental Research and Public Health,Yu-Tian Zhuang; Di-Xiang Xie; Xi-Jun Yu,"Rapid urbanization has led to a series of environmental problems that are harmful to the physical and mental health of residents. Not only can increasing urban tree canopy (UTC) improve sustainable city development, but it can also effectively promote the quality of life for residents; however, the uneven spatial distribution of UTC can also bring about social justice problems. Currently, there are few studies related to the justice of UTC distribution in China. Based on this, the paper adopts object-oriented image classification technology to interpret and extract UTC data from satellite images, and it uses house price to explore the justice of the spatial distribution of UTC in the main urban area of Guangzhou from the perspective of environmental justice by ANOVA, Pearson correlation coefficient, and bivariate local spatial autocorrelation. The results show that: (1) There is a significant positive correlation between UTC and house price in the main urban area of Guangzhou, and there are regional differences in the distribution of UTC, with the UTC in the “very high” house price group being significantly higher than that in other groups. (2) The spatial clustering of UTC and hou",10.0,https://www.mdpi.com/1660-4601/20/5/4050/pdf?version=1677228502
c43d4b29d222733ba6d2807aa49ea059ec866e4b,10.2196/16787,Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study,2020.0,JMIR Research Protocols,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,"Background 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. Objective 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. Methods 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. Results 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 people (55,150/229,75",10.0,https://www.researchprotocols.org/2020/5/e16787/PDF
94e043a4a4305f51d420a84f44054bde5a72e8a9,10.3389/fenvs.2022.957641,"What causes the spatiotemporal disparities in greenway use intensity? evidence from the central urban area of Beijing, China",2022.0,Frontiers in Environmental Science,Cai-Lin Qiu; N. Qiu; Tian-Jie Zhang,"The reasonable layout of green sports space has positive significance for promoting residents’ outdoor sports. Existing studies rarely use large-scale trajectory data to analyze urban greenway physical activity; they mostly use on-site research and questionnaires to conduct research. This study, using central Beijing as a case study, identified the spatiotemporal relationships of greenway use intensity with the social, economic, and built environment. A multiscale geographically weighted regression (MGWR) model was used to explore the temporal heterogeneity of the factors, compensating for the lack of cross-sectional data in existing studies. The results demonstrated significant differences in the spatiotemporal patterns among the core, central and inner suburban areas, and they revealed residents’ preferences in different periods. The MGWR model indicated that there were obvious scale differences and spatial heterogeneity in the greenway use intensity among social, economic, and built environment factors. Housing prices, residential density, historical resource density, and greening quality of greenways contributed to greenway use intensity, which was weak in the core area and str",10.0,https://doi.org/10.3389/fenvs.2022.957641
1bfcfbfa1c929625163693b222ca4d30ac584373,10.3390/land13122045,Land Use Characteristics of Commuter Rail Station Areas and Their Impact on Station Ridership: A Case Study of Japan Railways in the Tokyo Metropolitan Area,2024.0,Land,Ya-Nan Gao; Xu Cui; Xiao-Zhen Sun,"Exploring the relationship between land use characteristics and ridership in railway station areas provides crucial decision-making support for station area planning. Previous research has mostly focused on subways, with a lack of studies on the land use characteristics and ridership of commuter rail stations, particularly in relation to the differences and impacts across various passenger catchment areas (PCAs). This study employed a multinomial logit model to evaluate the land use characteristics within 1000 m of Japan Railways (JR) stations in four different PCAs of the Tokyo metropolitan area (TMA). Additionally, regression models and a multiscale geographically weighted regression (MGWR) model were used to analyze how land use characteristics in these PCAs affected station ridership. The key findings were as follows: (1) the land use characteristics around commuter rail stations exhibit distinct zonal patterns; within 250 m, public transport stops and public service facilities are the most densely concentrated; the highest residential population density is found between 250 and 750 m; and commercial facilities are mostly clustered in the 500 to 750 m range; (2) the impact of l",10.0,https://doi.org/10.3390/land13122045
87023a291cecb2341d80fd9327768265df64f73d,10.3390/ijerph18031202,Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health,2021.0,International Journal of Environmental Research and Public Health,K. Forthman; Janna M. Colaizzi; Hung-Wen Yeh; R. Kuplicki; M. Paulus,"Neighborhood characteristics can have profound impacts on resident mental health, but the wide variability in methodologies used across studies makes it difficult to reach a consensus as to the implications of these impacts. The aim of this study was to simplify the assessment of neighborhood influence on mental health. We used a factor analysis approach to reduce the multi-dimensional assessment of a neighborhood using census tracts and demographic data available from the American Community Survey (ACS). Multivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011–2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2014–2015 data (2017 release). A five-factor model provided the best fit for the data. Each factor represents a complex multi-dimensional construct. However, based on heuristics and for simplicity we refer to them as (1) Affluence, (2) Singletons in Tract, (3) African Americans in Tract, (4) Seniors in Tract, and (5) Hispanics or Latinos in Tract. African Americans in Tract (with loadings showi",10.0,https://www.mdpi.com/1660-4601/18/3/1202/pdf?version=1611916677
d3812508998e06d0728e9af8b9c2c72e4dad465a,10.1007/s11146-024-09997-w,Crime Measures and Housing Prices: an Analysis Using Quantile Regression and Spatial Autocorrelation,2025.0,Journal of real estate finance and economics,J. Kallberg; Yoshikiyo Shimizu,"Crime is a disamenity, so buyers should be willing to pay more for a house (all else equal) in a low crime area, suggesting that high crime rates depress housing prices. Conversely, it is plausible that criminals prefer wealthier areas because of the higher expected returns from their transgressions. This study examines the link between measures of crime and prices of residential housing. Our data begin in 2008 and end in 2020 for Seattle, Washington, including all reported felonies (756,304); 911 calls (1,528,303); all recorded residential real estate transactions (61,902 after filtering), as well as the corresponding property characteristics; demographic data and the associated changes. The inherent endogeneity between crime rates and housing prices forces us to find an instrument for crime rates. After rejecting several plausible choices, based on the Wu-Hausman test, the key variable in our empirical analysis is the number of 911 calls (contemporaneous and lagged) in a given beat. Our results present somewhat mixed evidence on the impact of crime on housing prices. Without adjustment for spatial autocorrelation, a 1 percentage point increase in crime rates (instrumented by the ",10.0,https://link.springer.com/content/pdf/10.1007/s11146-024-09997-w.pdf
b9a31e5026237365f502539781380854bd433e8d,10.1109/access.2021.3089198,A Real Estate Valuation Model Using Boosted Feature Selection,2021.0,IEEE Access,Kankawee Chanasit; E. Chuangsuwanich; A. Suchato; P. Punyabukkana,"To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be explained directly by model composition. In this research, we apply a combination of a boosting strategy and input sensitivity analysis in an improved Garson’s algorithm to perform feature selection that can adjust its selection criteria through each iteration on an ANN model. This proposed technique is then compared with other traditional feature selection techniques using synthetic data and real-world house valuation data. The results show that our model can maintain the sensitivity coefficient fo",10.0,https://ieeexplore.ieee.org/ielx7/6287639/9312710/09454508.pdf
df601ef1dccb2f3541f463c9108feb74ae6d227c,10.1080/13467581.2025.2455022,"Transcending the urban–rural dichotomy: inequality in urban green space availability among urban neighbourhoods, urban villages and rural villages in Guangzhou, China",2025.0,Journal of Asian Architecture and Building Engineering,Fubin Luo; Yun-Zhe Zhang; Lu-Yang Zhang,"ABSTRACT Urban green spaces (UGS) are integral to sustainable urban development, yet the urbanization process has resulted in unequal distributions of green resources across different neighbourhoods. In Chinese cities, there are three primary types of neighbourhoods with different socioeconomic features – rural villages (RV), urban villages (UV), and urban neighbourhoods (UN). They usually have different green space availability, posing challenges to environmental justice. However, the Green space inequality concerning neighbourhood types has not yet been fully explored. Drawing on the case of Guangzhou, we examine inequalities in UGS availability among these three types of neighbourhoods. We first explored the inequality in UGS availability among UN, UV and RV by employing the Gini and Theil indices and then used the eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanation (SHAP) explainer to elucidate the intricate association between neighbourhood socioeconomic statuses and UGS availability from a local perspective. Our findings indicate that UN and UV have similar levels of inequality in UGS availability, which is more severe compared to RV. Neighbourhoods",10.0,https://doi.org/10.1080/13467581.2025.2455022
08431c0329f8af4d67656a4a2ef7c0d688f3c9ee,10.3390/land14030476,Scale-Dependent Relationships Between Urban Morphology and Noise Perception: A Multi-Scale Spatiotemporal Analysis in New York City,2025.0,Land,Si-Ting Chen; Bing-Jie Yu; Guang Shi; Yiping Cai; Yanyu Wang; Pingge He,"Urban morphology significantly influences residents’ noise perceptions, yet the impact across different spatial and temporal scales remains unclear. This study investigates the scale-dependent relationship between urban morphology and noise perception in New York City using noise complaint rates (NCR) as a proxy for perceived noise levels. A multi-scale analysis framework was applied, including four spatial scales (100 m, 200 m, 500 m, and 1000 m) and three temporal classifications (daytime/nighttime/dawn, weekdays/weekends, and seasonal divisions). Statistical analyses, including Spearman correlation, Moran’s I test, and Geographically Weighted Regression (GWR), examined spatiotemporal heterogeneity. Results show: (1) NCR and urban morphology indicators vary significantly across spatial and temporal aggregations. (2) Correlations between NCR and urban morphology indicators generally strengthen with larger spatial units, revealing a scale effect. Temporal variations, e.g., residential land ratio (RES) and greenery percentage (SVI Green), show stronger correlations with NCR in summer than in winter. (3) The Moran’s I index revealed significant spatial clustering at the 1000 m scale.",10.0,https://doi.org/10.3390/land14030476
725553592d4da7f79a36a920bef6b5ae00a6cd2f,10.18196/jesp.v24i1.18016,Rupiah exchange rate: the determinants and impact of shocks on the economy,2023.0,Jurnal Ekonomi &amp; Studi Pembangunan,Erida Pratiwik; Sucihatinigsih Dian Wisika Prajanti,"The repetition of policy dynamics on Quantitative Easing (QE) and interest rate by The Fed potentially cause fluctuations in the exchange rate, including in Indonesia. Therefore, this study aims to analyze the determinants and impacts of exchange rate shocks. Inflation (INF), Money Supply (LJUB), Open Market Operations (OPT), Foreign Exchange Reserves (LCD), Expected Inflation (LEHU) and Interest Rates (SB) were used to analyze the determinants of Exchange Rate (NT) through Auto Regressive Distributed Lag (ARDL). The impact of NT shocks was analyzed using Vector Auto Regressive (VAR) by LEHU, Residential Property Price Index (PIHPR), Stock Transactions (LTRANS), and Banking Credit Volume (VK). The Expected Inflation variable and incorporation of ARDL-VAR are novelties in this study. In the secondary time series data for 2014M1 – 2022M9 period, the ARDL results showed that INF and LJUB had positive effect on NT in both long and short run, while OPT, LCD and SB had negative effect. LEHU had negative effect in the short run, but positive in the long run. The speed of adjustment in the model was 49.86% per month. Shock of NT had impacted VK until 15 months, PIHPR at 7 months, LTRANS at",10.0,https://journal.umy.ac.id/index.php/esp/article/download/18016/8392
65a97683efbca72a826c05351c4e3fba145739f6,10.5539/mas.v8n6p70,Affordable Housing Performance Indicators for Landed Houses in the Central Region of Malaysia,2014.0,,A. E. Labin; A. Che-Ani; S. Kamaruzzaman,"Recently, governments strive to make housing affordable for residents. Affordable housing is not restricted only to the house prices, but it includes also the quality and amenities of the house. So, the main aim of this research is to develop affordable housing performance indicators (AHPI) for landed houses. It's based mainly on Mulliner and Malienes criteria for affordable housing and the concept of grow home for Friedman and Cammalleri. Taman Selasih (TS) and Taman Lukut Makmur (TLM) in Negeri Sembilan were chosen as a case study. They were constructed by Syarikat Perumahan Negara Berhad (SPNB) in the central region of Malaysia. The sample consists of 155 units in TS and 93 units in TLM. A physical survey was conducted to assess the housing affordability for TS and TLM by field observation and informal interviews with the residents. The collected data were analyzed via SPSS software. The result shows that fourteen criteria can be applied as AHPI for landed houses, namely; houses prices in relation to income, safety- incidence of crime, access to employment, access to public transport facilities, access to good quality schools, access to shopping facilities, access to health care",10.0,https://doi.org/10.5539/mas.v8n6p70
f359608e2bb7db971453d8d6b663b7bf6c392269,10.3390/buildings12091345,Lead-Lag Relationship between the Price-to-Rent Ratio and the Macroeconomy: An Empirical Study of the Residential Market of Hong Kong,2022.0,Buildings,D. Lo; Y. Yau; M. McCord; M. Haran,"The price-to-rent (PtR) ratio is one of the most commonly used indicators to assess housing market conditions by policy makers and real estate practitioners. It is often employed as an economic barometer to detect whether a housing bubble exists and determine whether the property market has become unaffordable relative to historical trends. Despite a plethora of research studies on the PtR ratio in the housing literature, relatively little is known about its long-term dynamics with macroeconomic and financial determinants. By utilising time series data on the Hong Kong residential property market, this study examines the cointegration and causal relationships between a wide spectrum of macroeconomic indicators and the PtR ratios of housing segments of different tiers which comprise different socioeconomic groups of homebuyers and investors. The results point towards market compartmentalisation, in the sense that the PtR ratios of the housing submarkets respond to changes in macroeconomic fundamentals in a differential manner. For instance, the PtR ratios of housing segments with a greater proportion of owner-occupiers are statistically less y correlated with investment-related macr",10.0,https://www.mdpi.com/2075-5309/12/9/1345/pdf?version=1663646067
a69830481eb354b3666614b34ee77ba7a00124fb,10.3390/su12219142,"Home-Based Locational Accessibility to Essential Urban Services: The Case of Wake County, North Carolina, USA",2020.0,Sustainability,G. Shen; Zhangye Wang; Long Zhou; Yu Liu; Xiaoyi Yan,"Accessibility is an important concept in urban studies and planning, especially on issues related to sustainable transportation planning and urban spatial structure. This paper develops an optimization model to examine the accessibility from single family homes to major urban facilities for services or amenities using geographical information systems. The home-based accessibility to facilities is based upon the point to point direct distance from sampled homes to sampled facilities. Descriptive statistics about the accessibility, such as min/max, mean/median, and standard deviation/variance were computed. Variations of accessibility for a range of categories by home price and year built were also examined. Multivariate linear regression models examining the housing value with respect to home-facility accessibility by facility types were implemented. The results show that desirable urban facilities, which are also more frequently used for livability, enjoy better accessibility than undesirable urban facilities. The home-based accessibility’s positive or negative associations with home price along with year built and/or residential lot size exist for most facilities in general, and b",10.0,https://www.mdpi.com/2071-1050/12/21/9142/pdf?version=1605085904
b8de52a5afe34f73560c86d66de49847632edaaa,10.3390/f13091421,Can Indoor Residents Perceive Green and Blue Spaces in Communities as Posted Sentiments? A Verification in Nanchang,2022.0,Forests,Changhong Chen; Peng Guo,"Ecological infrastructures (EIs), such as public and urban green and blue spaces (GBSs), have been well demonstrated to benefit visitors’ mental well-being. Experiences in community GBSs may also evoke positive emotions for their residents. In this study, 54 communities in Nanchang were chosen as objective sites, where landscape metrics of GBSs were remotely evaluated. A total of 2105 local residents’ facial expressions (with happy, sad, and neutral emotions) were obtained from Sina Weibo. Inhabitants showed more net positive emotions (happy minus sad) in cold seasons, and females smiled more frequently than males. Newly constructed communities with houses for sale had larger areas of normalized difference vegetation index (NDVI) and built-up index compared to communities with no houses for sale. Neither the availability of houses for sale nor housing price had any effect on facial expression scores. Poisson regression revealed significant coefficients (β) of positive emotions with largeness of green space (GS) and blue space (BS). Overall, BS had a stronger contribution (β, 0.6–1.1) to residents showing positive emotions relative to GS (β, −2.45–0.89), whose area ratio of NDVI inc",10.0,https://www.mdpi.com/1999-4907/13/9/1421/pdf?version=1662385210
ffe166396b768e555cb55c1d7659e90af64b8233,10.3390/su9030365,A Geographic Information System (GIS)-Based Analysis of Social Capital Data: Landscape Factors That Correlate with Trust,2017.0,,S. Rahimi; Michael J. R. Martin; Eric Obeysekere; Daniel E. Hellmann; Xi Liu; Clio Andris,"The field of community sociology has yielded rich insights on how neighborhoods and individuals foster social capital and reap the benefits of interpersonal relationships and institutions alike. Traditionally, institutions and cultural factors have been lauded as catalysts of community social life and cohesion. Yet, the built environment and configuration of the landscape, including infrastructure, amenities and population density, may also contribute to community social capital. In this article, we embedded zip code-level responses from Harvard University’s Saguaro Seminar’s 2006 Social Capital Community Benchmark Survey with a geographic information system. Specifically, we correlated responses on residents’ general trust, trust of one’s neighbors, and trust of members of other racial groups with local urban environmental factors and infrastructural indicators such as housing and street conditions, land use, city form, amenity access (e.g., libraries and schools), home vacancy rates, and home value. We conducted these tests at the national level and for Rochester, NY, due to its many survey responses. We found that housing vacancies drive down levels of social trust, as captured ",10.0,https://www.mdpi.com/2071-1050/9/3/365/pdf?version=1489128727
aae551035d2e6f57a75829f22a35e1c1fcae982f,10.4236/oalib.1108346,Incorporating Multiple Linear Regression in Predicting the House Prices Using a Big Real Estate Dataset with 80 Independent Variables,2022.0,OALib,A. Abdulhafedh,"This paper uses a multiple linear regression analysis to predict the final price of a house in a big real estate dataset. The data describes the sale of individual properties, various features, and details of each home in Ames, Iowa, USA from 2006 to 2010. The dataset comprises of 80 explanatory variables which include 23 nominal, 23 ordinal, 14 discrete, and 20 continuous variables. The goal was to use the training data to predict the sale prices of the houses in the testing data. The most important predictors were determined by random forest and kept in the analysis. The highly correlated predictors were dropped from the dataset. All assumptions of the linear regression were checked, and an optimal final predictive model was achieved by keeping the most influential predictors only. The model accuracy assessments produced very good results with an adjusted R-squared value of 0.9283, a residual standard error (RSE) of 0.094, and a root squared mean error (RSME) of 0.12792. In addition, the prediction error (Mean Squared Error, MSE) of the final model was found to be very small (12%) by applying different cross validation techniques, including the validation set approach, the K-fold",10.0,https://doi.org/10.4236/oalib.1108346
4a61b53771906b04bbcb26ca9ab0193d7248ecba,10.3390/ijgi13030063,"A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece",2024.0,ISPRS Int. J. Geo Inf.,Polixeni Iliopoulou; Vassilios Krassanakis; Loukas-Moysis Misthos; Christina Theodoridi,"Short-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activity has not been adequately studied. In this paper, spatial data of Airbnb rentals in Athens are analyzed in order to indicate the factors which are important for the spatial variation of rents. Factors such as property capacity, host attributes and review characteristics are considered. In addition, several locational attributes are examined. Regression analysis techniques are used to predict the cost per night, according to various explanatory factors, while the results of two models are presented: ordinary least squares (OLS) and geographically weighted regression (GWR). The results of the OLS model indicate several factors determining the rent, including capacity and host characteristics, as well as locational attributes. The GWR model produces more accurate results with a smaller number of independent variables. For the residuals analysis several additional amenities were examined t",10.0,https://www.mdpi.com/2220-9964/13/3/63/pdf?version=1708425928
2ee804cd4788ca5b52b253a187d6163ac2f952b9,10.1108/ijhma-11-2019-0110,Cluster and forecasting analysis of the residential market in Turkey,2020.0,,Metin Vatansever; I. Demir; Ali Hepşen,"Purpose - The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices. Design/methodology/approach - In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices. Findings - The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort. Research limitations/implications - In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation. Practical i",10.0,
536b21e1d1ed00009b4d465d627862928baa148e,10.21799/frbp.wp.2019.06,How Big is the Wealth Effect? Decomposing the Response of Consumption to House Prices,2019.0,Working paper (Federal Reserve Bank of Philadelphia),S. B. Aruoba; Ronel Elul; S. Kalemli-ozcan,"We investigate the effect of declining house prices on household consumption behavior during 2006-2009. We use an individual-level dataset that has detailed information on borrower characteristics, mortgages and credit risk. Proxying consumption by individual-level auto loan originations, we decompose the effect of declining house prices on consumption into three main channels: wealth effect, household financial constraints, and bank health. We find a negligible wealth effect. Tightening householdlevel financial constraints can explain 40-45 percent of the response of consumption to declining house prices. Deteriorating bank health leads to reduced credit supply both to households and firms. Our dataset allows us to estimate the effect of this on households as 20-25 percent of the consumption response. The remaining 35 percent is a general equilibrium effect that works via a decline in employment as a result of either lower credit supply to firms or the feedback from lower consumer demand. Our estimate of a negligible wealth effect is robust to accounting for the endogeneity of house prices and unemployment. The contribution of tightening household financial constraints goes down t",10.0,https://doi.org/10.21799/frbp.wp.2019.06
5bf66ffc46efe90123937f0381f70ec140284f9f,10.3390/ijgi12070295,A New Algorithm for Large-Scale Geographically Weighted Regression with K-Nearest Neighbors,2023.0,ISPRS Int. J. Geo Inf.,Xiaoyue Yang; Yi Yang; Shenghua Xu; Jiakuan Han; Zhengyuan Chai; Gang Yang,"Geographically weighted regression (GWR) is a classical method for estimating nonstationary relationships. Notwithstanding the great potential of the model for processing geographic data, its large-scale application still faces the challenge of high computational costs. To solve this problem, we proposed a computationally efficient GWR method, called K-Nearest Neighbors Geographically weighted regression (KNN-GWR). First, it utilizes a k-dimensional tree (KD tree) strategy to improve the speed of finding observations around the regression points, and, to optimize the memory complexity, the submatrices of neighbors are extracted from the matrix of the sample dataset. Next, the optimal bandwidth is found by referring to the spatial clustering relationship explained by K-means. Finally, the performance and accuracy of the proposed KNN-GWR method was evaluated using a simulated dataset and a Chinese house price dataset. The results demonstrated that the KNN-GWR method achieved computational efficiency thousands of times faster than existing GWR algorithms, while ensuring accuracy and significantly improving memory optimization. To the best of our knowledge, this method was able to run ",10.0,https://www.mdpi.com/2220-9964/12/7/295/pdf?version=1690185501
caaf41026ae34028eef5803c958a7a2548bd26c0,10.15611/aoe.2017.1.06,Modeling of Residential Property Prices Index Using Committees of Artificial Neural Networks for Pigs the European-G8 and Poland,2017.0,,R. Wiśniewski,"This paper develops models of residential property prices indices for PIGS, the European-G8 and Poland using a committee of artificial neural networks approach. Quarterly time series data are applied for testing and the empirical results suggest that population growth, unemployment rate, final consumption expenditure, net national income (ANNI), household final consumption expenditure, long-term interest rates, HICP rate of change of housing, water, electricity, gas and other fuels, HICP housing services, HICP actual rentals for housing, and HICP maintenance and repair of the dwelling are the major determinants of the residential property price index in PIGS, the European-G8 and Poland. The developed models show that the economic and financial situation in a given country affects the residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe. The economic and financial crisis in the countries of the PIGS group affects not only the PIGS markets but also the real estate markets of the European-G8 and Poland. The results also suggest that a methodology based on a committee of artificial neural networks",10.0,https://doi.org/10.15611/aoe.2017.1.06
fe7d8da7d098fddf294eba3390c93569aa712723,10.3390/land11122224,Spatial–Temporal Evolution Patterns and Influencing Factors of China’s Urban Housing Price-to-Income Ratio,2022.0,Land,Wei Hu; Shanggang Yin; H. Gong,"The housing price-to-income ratio (PIR) is an important indicator for measuring the health of the real estate market and detecting residents’ housing affordability. Including data of 336 cities in China from 2009 to 2020 as the research unit, the PIR’s spatial and temporal evolution characteristics are explored by using the urban rank-size rule and Markov chain, and its influencing factors are explored using the random forest model. The results show the following: (1) The PIR is in a normal distribution pattern, and there was a significant positive spatial correlation, which tended to increase. (2) Spatially, the PIR shows an overall distribution trend of “high in the east and low in the west”, and a rising trend of fluctuation is shown in the average PIR. (3) The PIR’s time evolution has high stability. China’s urban PIR is primarily the stable type from 2009 to 2015 and mainly the upward transfer type from 2015 to 2020. (4) The influence of economic, demographic, social, and expected factors on the PIR decreases, among which real estate investment density, industrial structure level, residents’ consumption level, and real estate activity are the dominant factors enhancing trends ",10.0,https://www.mdpi.com/2073-445X/11/12/2224/pdf?version=1670910457
8d5b8b0c64167bc829668549c7b3bfd749bcd9ae,10.1109/icde60146.2024.00121,Target-agnostic Source-free Domain Adaptation for Regression Tasks,2023.0,IEEE International Conference on Data Engineering,Tianlang He; Zhiqiu Xia; Jie-Run Chen; Haoliang Li; S. G. Chan,"Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between the target and source using unlabeled target data. Source-free UDA removes the requirement for labeled source data at the target to preserve data privacy and storage. However, previous works on source-free UDA assume knowledge of domain gap, and hence is limited to either target-aware or classification task. To overcome it, we propose TASFAR, a novel target-agnostic source-free domain adaptation approach for regression tasks. Using prediction confidence, TASFAR estimates a label density map as the target label distribution, which is then used to calibrate the source model on the target domain. We have conducted extensive experiments on four regression tasks with various domain gaps, namely, pedestrian dead reckoning for different users, image-based people counting in different scenes, housing-price prediction at different districts, and taxi-trip duration prediction from different departure points. TASFAR demonstrates significant superiority over state-of-the-art source-free UDA approaches, achieving an average error reduction of 22 % across the four tasks and comparable accuracy to source-based UDA, all wi",10.0,https://arxiv.org/pdf/2312.00540
7c9e94c51f355093c71aa576a44eea5e9903d7a5,10.1609/aaai.v39i2.32167,EMPLACE: Self-Supervised Urban Scene Change Detection,2025.0,AAAI Conference on Artificial Intelligence,Tim Alpherts; S. Ghebreab; N. van Noord,"Urban change is a constant process that influences the perception of neighbourhoods and the lives of the people within them. The field of Urban Scene Change Detection (USCD) aims to capture changes in street scenes using computer vision and can help raise awareness of changes that make it possible to better understand the city and its residents. Traditionally, the field of USCD has used supervised methods with small scale datasets. This constrains methods when applied to new cities, as it requires labour-intensive labeling processes and forces a priori definitions of relevant change. In this paper we introduce AC-1M the largest USCD dataset by far of over 1.1M images, together with EMPLACE, a self-supervising method to train a Vision Transformer using our adaptive triplet loss. We show EMPLACE outperforms SOTA methods both as a pre-training method for linear fine-tuning as well as a zero-shot setting. Lastly, in a case study of Amsterdam, we show that we are able to detect both small and large changes throughout the city and that changes uncovered by EMPLACE, depending on size, correlate with housing prices - which in turn is indicative of inequity.",10.0,https://doi.org/10.1609/aaai.v39i2.32167
bb52b42d7df9da3149b3e4c587e390f1c6734244,10.1016/j.resourpol.2020.101821,Valuing shale gas development in resource-dependent communities,2020.0,,Zachary T. Keeler; H. Stephens,"Abstract Technological advances in oil and gas drilling have enabled the productive extraction of natural gas in new regions. The benefits from employment and income opportunities can help stimulate economies and may be valued by local residents. At the same time, however, shale gas activity can alter natural landscapes and is associated with negative externalities, including potential groundwater contamination. While some previous research has examined the impact of shale development, our paper focuses on the local impacts in West Virginia, a state with a long history of resource-extraction and one whose economy has lagged the nation. Because of its history of resource extraction, communities in West Virginia who may have limited other economic prospects may value the activity differently. Additionally, most of the previous research used data during the initial boom, ignoring the slowdown that followed. Using the coarsened exact matching (CEM) technique, we match houses near producing wells with other similar houses, in order to examine how property values in West Virginia are affected by proximity to horizontal producing wells. This technique helps compensate for the relatively s",10.0,
80b9b26e4623153edc822ea6d0341e3725b68f82,10.33093/ijomfa.2021.2.1.1,The Determinants of House Prices in Malaysia,2021.0,International Journal of Management Finance and Accounting,Hazmi Hamizan bin Mohd Zaki,"This paper studied how house prices were affected by macroeconomic factors from Q1 2009 to Q4 2018. The short and long-run effects of real income, nominal interest rates, inflation rate and stock prices on house prices in Malaysia were examined with the autoregressive distributed lag (ARDL) of a restricted error correction model (ECM). It was discovered that the selected macroeconomic factors were cointegrated with house prices. Income, represented by real Gross Domestic Product (GDP), significantly affected house prices in the short and long-run. Inflation and interest rate, proxied by Consumer Price Index (CPI) and Overnight Policy Rate (OPR), respectively, affected house prices significantly in the long-run. The stock market, tracked by Kuala Lumpur Composite Index (KLCI), had no significant impact on house prices signifying no wealth effect. Through the findings of an inelasticity of demand and an undesirable result of monetary policies, this paper concluded that more effective solutions needed to be carried out to ensure affordability of house ownership in Malaysia.",10.0,https://journals.mmupress.com/index.php/ijomfa/article/download/55/27/569
85e991e5a5469b240d2036a499d834301b056b50,10.1007/s40300-021-00223-8,A Bayesian piecewise linear model for the detection of breakpoints in housing prices,2021.0,Metron,J. Tomal; Hafizur Rahman,"Statistical thresholds occur when the changes in the relationships between a response and predictor variables are not linear but abrupt at some points of the predictor variable values. In this paper, we defined a piecewise-linear regression model which can detect two thresholds in the relationships via changes in slopes. We developed the corresponding Bayesian methodology for model estimation and inference by proposing prior distributions, deriving posterior distributions, and generating posterior values using Metropolis and Gibbs sampling algorithm. The parameters in our model are easy to understand, highly interpretable, and flexible to make inferences. The methodology has been applied to estimate threshold effects in housing market pricing data in two cities - Kamloops and Chilliwack - in British Columbia, Canada. Our findings revealed that the implementation of changes in the government property tax policies had threshold effects in the market price trend. The proposed model will be useful to detect threshold effects in other correlated time series data as well.",10.0,https://link.springer.com/content/pdf/10.1007/s40300-021-00223-8.pdf
2b852239cb806d6b616e6bf577d401c6e5a443f9,10.21837/pm.v19i16.953,APPLICATION OF MACHINE LEARNING IN ANALYSING HISTORICAL AND NON-HISTORICAL CHARACTERISTICS OF HERITAGE PRE-WAR SHOPHOUSES,2021.0,PLANNING MALAYSIA,Nur Shahirah Ja’afar; Junainah Mohamad,"Real estate is complex and its value is influenced by many characteristics. However, the current practice in Malaysia shows that historical characteristics have not been given primary consideration in determining the value of heritage properties. Thus, the accuracy of the values produced is questionable. This paper aims to determine whether the historical characteristics of the pre-war shophouses at North-East Penang Island, Malaysia contribute any significance to their value. Several Machine Learning algorithms have been developed for this purpose namely Random Forest, Decision Tree, Lasso Regression, Ridge Regression and Linear Regression. The result shows that the Random Forest Regressor with historical characteristics is the best fitting model with higher values of R-squared (R²) and lowest value of Root Mean Square Error (RMSE). This indicates that the historical characteristics of the heritage property under study contribute to its significant value. By considering the historical characteristics, the property’s value can be better predicted.",10.0,https://www.planningmalaysia.org/index.php/pmj/article/download/953/658
9c0c1cd0fb5ec870d4677c2ef9d5aaf83befb45a,10.1177/0886260519849693,"The Effects of Risky Places, Motivated Offenders, and Social Disorganization on Sexual Victimization: A Microgeographic- and Neighborhood-Level Examination",2019.0,Journal of Interpersonal Violence,R. Konkel; Alexis J Hafemeister; L. Daigle,"We test microgeographic (i.e., street segment) effects reflective of routine activities/lifestyle theories (e.g., the locations of sex offenders, gang members, drug houses, bars) and neighborhood-level (i.e., block group) effects consistent with social disorganization theory (e.g., socioeconomic disadvantage, residential mobility, racial heterogeneity) on the locations of sexual offenses within one rural and one suburban city. We employ multilevel modeling to examine neighborhood (N = 41) and microgeographic (N = 1,382) effects on sex offenses. Neighborhood context was associated with the locations of sex offenses in which disadvantage, concentrated poverty, racial heterogeneity, violent crime rates, and suburban (vs. rural) areas were positively associated with sex offense rates, whereas residential mobility had a negative effect on sex offense rates. Microgeographic context also explained variation in sex offense rates in which gang members, sex offenders, drug houses, and high population housing communities had positive effects on sexual assault rates, while controlling for spatially lagged effects. Finally, the effect of high-density housing communities was moderated by the lev",10.0,
fd6c4b15d3efa43ecc1047dda34c16af33dd7a3a,10.1007/s00168-025-01365-7,Boosting the accuracy of property valuation with ensemble learning and explainable artificial intelligence: The case of Hong Kong,2025.0,The annals of regional science,Lin Deng; Xue-Qing Zhang,"This paper proposes a novel three-level ensemble learning model to boost the accuracy of property valuation. Compared with current machine learning models, our ensemble learning strategies perform better. Explainable artificial intelligence methods are applied to identify significant housing price determinants. The results show that most features have nonlinear relationships with price. The property’s age and geographic coordinates are the most important features and the density and diversity of urban amenities positively relate to price. The number of building blocks or housing units shows a threshold effect on price, and a price premium of higher public transit accessibility is witnessed. Implications for future property development and urban planning are provided regarding building rehabilitation and renewal, property development preparation and urban center identification, land use planning and architectural design, integrated development of metropolitan amenities, and transit-oriented development.",10.0,https://doi.org/10.1007/s00168-025-01365-7
5fcfc291f2b822d53d4975129a07af7b8420dfc7,10.3390/su11092482,Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development,2019.0,Sustainability,Lin-Lin Zhao; J. Mbachu; Zhan-Sheng Liu,"The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions.",10.0,https://www.mdpi.com/2071-1050/11/9/2482/pdf?version=1556434240
d78766c98123ebd07f3bd891502e62c0550abb86,10.21837/pmjournal.v16.i6.466,FACTORS DETERMINING THE DEMAND FOR AFFORDABLE HOUSING,2018.0,PLANNING MALAYSIA JOURNAL,X. Lim; A. Olanrewaju; Seong Yeow Tan; Jia-En Lee,"Affordable housing has been defined as housing which is adequate in quality and location. In addition to this, it is deemed to be housing that is not so costly that it prevents its occupants from meeting their basic living needs. This study aims to develop a framework to facilitate affordable housing delivery. The data collection used was a survey questionnaire. The survey was administered to occupants within five high-rise buildings in Penang. The study also utilized a Kaiser-Meyer-Olkin measure of 0.518, and Bartlett’s test of sphericity of (x2 (210) = 10953.982, p<0.001). The two statistical test discovered that the major determinants affecting demand for affordable housing were crime rates, housing prices and down payments. The results also demonstrated that six factors were successfully constructed using a factor analysis and assigned as factors that determined the demand for affordable housing. The research will be useful to policy makers, urban planners, developers, and contractors.",10.0,https://doi.org/10.21837/pmjournal.v16.i6.466
c78288c38ff4eb563cbe921cb2774a82731f52bd,10.1016/j.landusepol.2021.105443,"Is it the flood, or the disclosure? An inquiry to the impact of flood risk on residential housing prices",2021.0,Land Use Policy,Lin-Han-Chiang Hsieh,"Abstract In 2015, the government of Taipei, Taiwan, published a series of flood risk maps based on simulation results. It sparked a broad public debate on flood risk information and the real estate market. This study therefore investigates the effect of making flood risk information public on residential housing prices and makes the following contributions. The first contribution concerns public policy. By analysing the relationship between the published flood risk information and more than 12,000 central Taipei sales records in 2015 and 2016, this study finds that the effect of flood risk on housing prices is significant and negative both before and after the maps were disclosed. Although some local variation in the effect of flood risk after the map disclosure was identified, there is no evidence that the disclosure altered the effect on housing prices. This finding suggests that the disclosure of the flood risk maps impacted the effect of the flood risk locally without influencing the market as a whole. Another contribution concerns perception. The result indicates that homebuyers’ negative attitude towards flood risk mainly focuses on the risk under low-intensity, high-frequenc",10.0,
407913d5703b5a98b79dc546a35a67847b41c4ac,10.1016/j.sepro.2011.08.033,Spatial Autoregressive Model of Commodity Housing Price and Empirical Research,2011.0,,F. Lan; Yuan Zhang,"Abstract Based on spatial econometric model, the article selects the panel data of eight cities around Beijing from 1998 to 2009. It tests whether there is spatial dependence among cities on commodity housing price. The main influence factors of the housing price are further analyzed. Finally, the housing prices of the 8 cities are tested by the Granger. The results show that there is significant spatial dependence between cities on the housing price. The factors that affect the commodity housing price include spatial factor, urban residents’ disposable income factor, population factor, land price factor and living space factor. Granger test shows that there is one-way relationship of Beijing to Tianjin, Shijiazhuang, Shenyang, Changchun and Jinan. The conclusions establish the theoretical foundation for the formation mechanism of the housing price and offer references for engineering project pricing in real estate and government macro regulation.",10.0,https://www.sciencedirect.com/science/article/pii/S2211381911000348/pdf
8ccf6e7abf93a5236bfbf2595bef0a062f63f0d7,10.30596/ekonomikawan.v17i2.1794,Determinan Yang Mempengaruhi Jumlah Permintaan Kredit Pemilikan Rumah di Indonesia,2017.0,,Rifki Khoirudin,"Bank lending to the consumption sector has increased dramatically since Indonesia was hit by the economic crisis ten years ago. This happens because many large companies go bankrupt so that the corporate sector absorbs very little credit from banks. Banks are increasingly recognizing that opportunities in the consumer market are growing, where the risks they face are relatively smaller compared to those in the investment market. Assuming that the house is a basic necessity that does not have substitutes, the possibility for a consumer who wants to own a home either in cash or credit is simply adjusting the type of home that suits the ability with different income levels by each individual. In this research, the method used is multiple linear regression. From independent variable that is economic growth, loan interest rate and residential property price index able to influence dependent variable that is mortgage credit equal to 97,9%",10.0,http://jurnal.umsu.ac.id/index.php/ekawan/article/download/1794/pdf_82
c058474dbcfb5eff1be29704257fa621e3cc801d,10.1177/00420980231182074,The impact of human capital and housing supply on urban growth,2023.0,Urban studies,Simon Büchler; Dongxiao Niu; A. Thompson; Si-Qi Zheng,"We empirically analyse the impact of human capital and housing supply on urban growth in the US and China. Integrating the heterogeneity of housing supply helps determine how a positive human capital shock translates into more population, higher house prices, or higher wages. To causally estimate this effect, we use a rich urban-level data set, choose our controls using the post-double-selection methodology, and instrument human capital with the per capita number of historical educational institutions. We find that human capital positively impacts urban population, house price and wage growth. While an elastic housing supply reinforces the impact on urban growth, it reduces house price growth and wage growth. Our results infer that human capital increases productivity in both countries and acts as an amenity only in the US.",10.0,https://journals.sagepub.com/doi/pdf/10.1177/00420980231182074?download=true
6244abcfaf4ad1ae4a4d6ff086a7f6c2da1ebe48,10.1016/j.cities.2019.102403,"Is there room for children in booming western cities? Empirical evidence from Austin, Denver, and Portland",2020.0,,J. Wegmann,"Abstract Austin, Denver, and Portland are all booming cities in or on the edge of the American West. Their thriving economies and natural and urban amenities have attracted large numbers of in-migrants. As housing prices rise, families with children in particular face diminished choices about where to live. This article asks three questions: How have the child populations of Austin, Denver, and Portland fared in recent decades? Is there a link between gentrification and a decrease in family households with children? And finally, to what extent do various housing types associate with more or fewer of these households? In brief, Austin, Denver, and Portland have fared reasonably well in maintaining family life, but neighborhoods with master-planned brownfield or greenfield developments appear to have accounted for a disproportionate share of the growth in their child populations, helping to offset sharp losses in gentrifying neighborhoods closer to the cities' urban cores. As these opportunities begin to diminish in all three cities, the strong association between compact single-family and “missing middle,” or middle density, housing types and households with children suggests pathwa",10.0,
da00f4f5f131a1ad12b76bbcc275108d34a64515,10.1080/00167223.2021.2019073,Exploring the distribution of city street greenery from eye-level: an application of Baidu Map panoramic images data,2021.0,Danish journal of geography,Chen-Li Xue; Cheng Jin; Liangchen Zhou; Guan-Nan Li,"ABSTRACT Increasing attention has been paid to the analysis of street greenery using the Green View Index (GVI). Current methods of vegetation extraction are not suitable for processing large amounts of data. This study introduces Visible Difference Vegetation Index (VDVI) to process Baidu Map panoramic static images which is relatively convenient for GVI calculation. The method is used to analyse the spatial distribution of street greenery in Guangzhou city in China. The results show that: (1) The spatial distribution of GVI values in Guangzhou show obvious agglomeration. (2) The average level and evenness of street greenery distribution is analysed. Tianhe and Yuexiu districts had high levels of evenly distributed street greenery; Liwan, Haizhu, Huangpu, and Panyu districts had good levels of street greenery but uneven distributions; Baiyun and Nansha had low levels of street greenery. (3)There is a significant correlation between road grade, house price, building age, land use and GVI values, which reveals the driving factors of GVI value distribution in Guangzhou. The GVI calculation method in this method is relatively convenient and accurate. GVI can be a new evaluation tool f",10.0,
b6dafdfd9f520fba28bba5b6d66881de6571b1e0,10.1177/0361198120911054,Impact of Transit-Oriented Development on Residential Property Values around Urban Rail Stations,2020.0,,Yang Jiang; Pei-Qin Gu; Zhejing Cao; Yu-Lin Chen,"Few previous studies have linked transit-oriented development (TOD) patterns and property values at the station level in the Chinese context. Using Shanghai as a case study, we obtained 21,094 transaction records within the 800 m radius catchment areas of 280 urban rail stations from a local property agency. A performance-based TOD indicator framework was established to capture built-environment features for density, diversity, and design dimensions. Using open-source geospatial data, we generated a series of TOD indicators for each station catchment area and further developed a composite TOD index with information entropy weighting. The results of multilevel regression models showed that within urban rail station catchment areas, residential properties with higher TOD levels were positively associated with higher value. In addition, increasing pedestrian and bike accessibility and integrating diversified urban functions around stations could create more value for nearby residential properties.",10.0,
64d4dd1b0a85c05dc7f80b4cf189703862285163,10.1080/02673037.2020.1736520,Long-term housing affordability in spatial general equilibrium,2020.0,,M. Beenstock; D. Felsenstein; X. Dai,"Abstract We argue that housing affordability is as much about incomes as it is about house prices. Consequently, a comprehensive analysis of housing affordability should be conducted in which incomes and house prices are determined through the specification of labor, capital and product markets in addition to housing markets. A spatial econometric model for Israel is used to study the effects on the regional distribution of housing affordability of income generating shocks in labor and capital markets, as well as supply shocks in housing markets. Particular attention is paid to the effects on affordability of planning delays in tendering land for housing construction and the issue of building permits. Spatiotemporal impulse responses for housing affordability show that region-specific shocks, such as accelerated planning permission and the provision of regional investment grants, percolate across the economy as a whole. Implications for place-based regional policy are discussed.",10.0,
7745824548c1ccfa21c0b9386d950bb794481e58,10.1109/icmi55296.2022.9873784,Artificial Intelligence Approach for Modeling House Price Prediction,2022.0,International Conference on Multimodal Interaction,Melihsah Cekic; Kübra Nur Korkmaz; Habib Müküs; A. Hameed; Akhtar Jamil; Faezeh Soleimani,"Real estate has a vast market volume across the globe. This domain has been growing significantly in the past few decades. An accurate prediction can help buyers, and other decision-makers make better decisions. However, developing a model that can effectively predict house prices in complex environments is still a challenging task. This paper proposes machine learning models for the accurate prediction of real estate house prices. Furthermore, we investigated the feature importance and various data analysis methods to improve the prediction accuracy. Linear Regression, Decision Tree, XGBoost, Extra Trees, and Random Forest were used in this study. For all models, hyperparameters were first calculated using k-fold cross-validation, and then they were trained to apply to test data. The models were tested on the Boston housing dataset. The proposed method was evaluated using Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics.",10.0,
29950be0deecc8c7f4b21b18e5126a14bb3669a2,10.1080/00036846.2019.1683146,The value of school in urban China: a spatial quantile regression with housing transactions in Beijing,2020.0,Applied Economics,Leiju Qiu; Dongmei Guo; Xuankai Zhao; Wenzhan Zhang,"ABSTRACT This study adopts microdata of individual housing transactions in Beijing to explore the value that households place on high-quality schools. It is among the first to match exactly the housing unit with its corresponding school in studies on Chinese housing markets. We find a significant school premium of 21.1% in Beijing’s housing market after controlling the unobservable neighbourhood effects using regressions with a spatial lag. We have further discovered different school premiums across quantiles with a spatial quantile regression. The result finding a larger school premium in the lower-priced housing units suggests that low-income households prefer high-quality schools over housing consumption. Our results indicate that public education influences households’ residential location choice and housing consumption decisions, which thus have important policy implications in public goods provision, housing development and urban planning.",10.0,
8198ec480b8ed93893d4c65bd5d436c9dc456af8,10.1080/17421772.2022.2123553,Bayesian inference in spatial GARCH models: an application to US house price returns,2022.0,Spatial Economic Analysis,O. Doğan; Suleyman Taşpınar,"ABSTRACT In this paper we consider a high-order spatial generalized autoregressive conditional heteroskedasticity (GARCH) model to account for the volatility clustering patterns observed over space. The model consists of a log-volatility equation that includes the high-order spatial lags of the log-volatility term and the squared outcome variable. We use a transformation approach to turn the model into a mixture of normals model, and then introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation approach coupled with a data-augmentation technique. Our simulation results show that the Bayesian estimator has good finite sample properties. We apply a first-order version of the spatial GARCH model to US house price returns at the metropolitan statistical area level over the period 2006Q1–2013Q4 and show that there is significant variation in the log-volatility estimates over space in each period.",10.0,
f1706ff5c106c27f641f1693db1c4e0afced7dbc,10.1109/icecet52533.2021.9698641,"Explainable, Multi-Region Price Prediction",2021.0,"2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)",Atharva Ghatnekar; A. Shanbhag,"Today, Machine Learning has risen to prominence in a variety of fields, from medical diagnosis to stock market trading. Working in the same line, we have focused our paper on one such application of machine learning. In this paper, we have implemented machine learning algorithms to develop prediction forms for house prices based on actual data of real estate in Boston and California. A comparative study of house prices, this paper first introduces the learning algorithms used. Then, multiple regression models and their optimized parameter values are discussed. Basis the prediction results, the paper first compares the performance of Machine Learning models through quantitative metrics, evaluates the importance of attributes influencing the valuation of houses in Boston and California through SHAP values, and finally draws a conclusion on the real estate prices using economic reasoning.",10.0,
9f78b302b1ac7f326ade807408fa0224e2e3c9d3,10.1109/icseiet58677.2023.10303359,Housing Price Prediction Model Using Machine Learning,2023.0,2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET),A. Chaurasia; Inam UI Haa,"Housing price prediction is a challenging task due to the complexity of huge data variance with changes in location points. In this research paper, we propose a machine learning- based house pricing prediction model that can predict the prices of houses more accurately. The proposed model uses a combination of data pre-processing techniques and machine learning algorithms simultaneously. The efficiency of the proposed model is evaluated using real-time house price data, and the results show significant improvement over the existing techniques.",10.0,
fc4adb104da33aa77afd4e5c1b5fb9e9b19a17fd,10.2139/ssrn.288353,Spatial Prediction of House Prices Using LPR and Bayesian Smoothing,2001.0,,John M. Clapp; Hyon‐Jung Kim; A. Gelfand,,10.0,https://doi.org/10.2139/ssrn.288353
1966a69528e5826bf46d2e134043438cc709aaef,10.1016/j.habitatint.2024.103250,Explaining housing rents: A neural network approach to landscape image perceptions,2025.0,Habitat International,Xiaorui Wang; Ji-Hui Yuan; Yangcheng Gu; Daisuke Matsushita,,10.0,https://doi.org/10.1016/j.habitatint.2024.103250
f993176ad78e5de60dbd59429d1ddcc5432c9e95,10.1007/s41060-024-00682-y,Graph neural networks for house price prediction: do or don’t?,2024.0,International Journal of Data Science and Analysis,Margot Geerts; S. Vanden Broucke; Jochen De Weerdt,,10.0,https://lirias.kuleuven.be/retrieve/cacb25c7-ab31-446b-b976-ff006530dbf6
d1ccbc3624629ca13730a12d343d3f7d4b589acf,10.2139/ssrn.1645156,Spatial Autoregressive Models for House Price Dynamics in Italy,2010.0,,Tiziana Caliman; E. di Bella,,10.0,https://doi.org/10.2139/ssrn.1645156
b2bb9d9603d2395c227288c30b42536bbb96376d,10.2139/ssrn.2205110,The Dynamics of House Price Capitalization and Locational Sorting: Evidence from Air Quality Changes,2012.0,,Corey Lang,,10.0,https://doi.org/10.2139/ssrn.2205110
e80ce8d9b22d671aa3ae4dbc3b6dcebd405cca2f,10.1109/ams.2010.29,Estimation of Housing Prices by Fuzzy Regression and Artificial Neural Network,2010.0,2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation,R. Ghodsi; A. Boostani; F. Faghihi,,10.0,
34665b62127b960534403518138bf649ef2625d4,10.1080/09599916.2013.765499,Implications of rising flood-risk for employment location: a GMM spatial model with agglomeration and endogenous house price effects,2013.0,,Yu Chen; B. Fingleton; G. Pryce; Albert S. Chen; S. Djordjević,,10.0,
8c767bffc71b3ac653a80ca6d0a65b169e18bc20,10.1007/s12061-025-09638-6,Assessing the Combinational Effects of Access to Urban Amenities on Housing Prices: A Perspective on the “15-Minute City”,2025.0,Applied spatial analysis and policy,Peiyu Tian; Weiye Xiao; Feng Yuan,,10.0,
771145e1d79e9d0399823a474ccbd76d57434104,10.1080/03610920701858404,Bayesian Spatial Modeling of Housing Prices Subject to a Localized Externality,2008.0,,M. Ecker; V. D. de Oliveira,,10.0,
87e810f2a3dd35b9b74b69d4277557b399a27db6,10.7763/joebm.2015.v3.208,Determinants of Hong Kong's Housing Prices,2015.0,,A. Cheng; M. Fung,,10.0,
0f9d81a090cc6c05b9318940b15f970e70b93622,10.1016/j.neucom.2013.07.035,Semiparametric spatial effects kernel minimum squared error model for predicting housing sales prices,2014.0,Neurocomputing,J. Shim; Okmyung Bin; Changha Hwang,,10.0,
9aeceed46c85cf2b28a0e2273186f0718686b155,10.7326/m18-2800,Local area variation in morbidity among low-income older adults in the United States,2019.0,Annals of Internal Medicine,M. Polyakova; Lynn M. Hua,"Supplement. Supplemental Material Geographic variation in mortality and morbidity and the association between socioeconomic status and health are both widely recognized (111). Researchers have found that there are substantial differences in life expectancy and in causes of death across U.S. geographic regions, even after accounting for differences in age, sex, and racial composition (1224). A separate, large literature has reported differences in mortality and morbidity between different socioeconomic and demographic groups in the United States (13, 16, 22, 2538). More recently, substantial differences in the income-longevity association were found across geographic locations (39, 40). Researchers have documented that adults with incomes in the first quartile of the income distribution living in more affluent areas of the United States have higher life expectancies (39). To our knowledge, limited evidence exists on the local area variation in morbidity stratified by income using clinical measures of diagnoses and large samples of the U.S. population. Using 100% of Medicare administrative records, we aimed to assess the association between the prevalence of chronic conditions among ",9.0,https://europepmc.org/articles/pmc7062581?pdf=render
bdd4b865442b53764395174fa2869b006d5d3293,10.1108/pm-08-2017-0051,Does “clean” pay off? Housing markets and their perception of heating technology,2018.0,Property Management,J. Hahn; J. Hirsch; S. Bienert,"
Purpose
The purpose of this paper is to investigate the role of distinct types of heating technology and their price impact in German residential real estate markets, considering a wide range of other housing market determinants. The authors aim to test and to verify specifically, whether the obsolescence of heating technology leads to a significant price discount and whether higher technological standards (and environmental friendliness) come with a price premium on the market.


Design/methodology/approach
The authors create housing market models for rental and sales segments by constructing generalized additive models with explicit multi-layered spatial components. To elaborate a profound and contemporary answer using these models, the authors perform large-sample regression analyses based on more than 400,000 observations covering German residential properties in 2015.


Findings
First and foremost, the heating system indeed shows significant explanatory importance for measuring housing rents and purchasing price. Second, the authors find that it makes a difference whether clean “green” technologies are implemented or whether “brown” systems with obsolete technology or fossil ",9.0,https://doi.org/10.1108/pm-08-2017-0051
4a1815db0ff486c6557e5a02db48d96f8e76d0ce,10.1108/ijhma-01-2025-0004,Real estate decision-making: precision in price prediction through advanced machine learning algorithms,2025.0,International Journal of Housing Markets and Analysis,Mohammad A. Shbool; Rand H. Al-Dmour; B. Al-Shboul; N. Albashabsheh; Najat Almasarwah,"Purpose
This study aims to enhance real estate price prediction accuracy using advanced machine learning models, minimizing biases and inconsistencies inherent in traditional appraisal methods. By leveraging support vector regression (SVR) and gradient boosting machine (GBM), this study provides a data-driven approach to property valuation, improving decision-making for buyers, sellers and policymakers. This study also seeks to bridge the gap in machine learning applications for emerging markets like Jordan. This study’s research’s broader goal is to offer a transparent, efficient and reliable tool for property valuation that improves market efficiency and reduces transaction uncertainty.

Design/methodology/approach
This study uses machine learning techniques – SVR and GBM – to predict real estate prices in Amman, Jordan. Data was collected from the Department of Lands and Survey, covering residential property sales from March 2023 to December 2023. The data set underwent preprocessing, including one-hot encoding for categorical variables and logarithmic normalization for skewed data. Hyperparameter tuning was performed using grid search, and an ensemble approach compared multiple",9.0,https://figshare.com/articles/journal_contribution/Real_estate_decision-making_precision_in_price_prediction_through_advanced_machine_learning_algorithms/29161865
ed5e0264a985e5aca214b7bd88d3f736ebab6ef1,10.1016/j.envres.2024.118625,The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory.,2024.0,Environmental Research,T. Lam; N. D. den Braver; H. Ohanyan; A. Wagtendonk; I. Vaartjes; J. Beulens; J. Lakerveld,"BACKGROUND
Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments.


OBJECTIVES
We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods.


METHODS
The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (Bray et al., 2017)",9.0,https://doi.org/10.1016/j.envres.2024.118625
25c41abe2124ef4ff63b17907390afa5a40ecb35,10.1186/s41118-023-00203-y,From South Asia to Southern Europe: a comparative analysis of Sri Lankans’ residential segregation in the main Italian cities using high-resolution data on regular lattice geographies,2023.0,Genus,Francesca Bitonti; F. Benassi; Angelo Mazza; Salvatore Strozza,"The present work proposes a spatial analysis of the residential segregation and settlement models of Sri Lankans in the eight main Italian municipalities. Hosting more than half of the total Sri Lankan population residing in Italy, the selected urban areas allow Sri Lankans’ residential model to be globally framed across the country. The purpose of this work is threefold. First, it provides a general assessment of the allocation pattern of a foreign community that has seldom been studied and yet is characterized by peculiar settlement choices. Second, it attempts to compare the settlement patterns of Sri Lankans across different urban contexts. Third, it aims to detect the possible spatial polarization of Sri Lankans in specific neighbourhoods and to verify its spatial correlation with other key variables that constitute proxies of urban neighbourhoods’ socioeconomic inequalities. The study runs multiple aspatial linear models to assess the global variation in concentrations of Sri Lankans related to several socioeconomic predictors. Furthermore, it implements geographically weighted regressions to explicitly model the spatial dependence between Sri Lankans’ location quotients and ",9.0,https://genus.springeropen.com/counter/pdf/10.1186/s41118-023-00203-y
70ec1866934fbbf3b9815cb5f8054e06db8ec36d,10.32604/cmc.2025.061400,Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions,2025.0,Comput. Mater. Continua,Faisal Ghazi Beshaw; T. Atyia; M. Salleh; M. Ishak; Abdul Sattar Din,": The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest Regressor. These models were deployed with the use of Shareable, Plot-interpretable Explainable Artificial Intelligence techniques, to improve trust in the AI. The findings suggest that our developed models are superior to the conventional models discussed in prior studies; with high Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values being close to perfection. In detail, the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 RMSE. S",9.0,https://cdn.techscience.press/files/cmc/2025/TSP_CMC-83-2/TSP_CMC_61400/TSP_CMC_61400.pdf
c827a3e790257dbb4cecb18e8d32dd8ef3b878bd,10.3390/land11060804,The Impact of Brownfields on Residential Property Values in Post-Industrial Communities: A Study from the Eastern Part of the Czech Republic,2022.0,Land,Kamila Turečková; Stanislav Martinát; J. Nevima; František Varadzin,"Dilapidated, neglected, or abandoned sites and buildings (so-called brownfields) are frequently neither visually attractive nor aesthetically valuable. Indeed, neglected brownfields contribute to the emergence of both objective and subjective barriers to the social, economic, and environmental development of communities. We also know that the occurrence of decayed brownfields affect the prices of residential housing in their vicinity. In our paper, we seek to better understand the impact of brownfields on the perceived value of neighbouring properties. We are also keen to shed more light on the factors behind the allegedly undesirable impact of brownfields on property values. Our research is based primarily on data obtained from a survey of 1,152 respondents in ten municipalities situated at the eastern part of the Czech Republic, where neglected brownfields frequently occur. The findings are complemented by an analysis of sales of residential properties located in proximity to brownfield sites, to further confirm our preliminary survey results. The results confirmed that neglected brownfields tend to negatively affect the value of neighbouring residential properties. Our results a",9.0,https://www.mdpi.com/2073-445X/11/6/804/pdf?version=1654513868
b6b83bd544c264c3a34ebfa793244a6bee45c11c,10.1007/s11269-010-9583-2,Determinants of Water Connection Type and Ownership of Water-Using Appliances in Ireland,2010.0,,S. Lyons; J. O’Doherty; R. Tol,"Developing optimal policies on management of water resources, investment in relevant infrastructure and the protection of the environment requires data on the current and likely future demand for water services. In jurisdictions without water metering, information on the factors influencing demand tends to be limited. Microdata from household surveys can provide some relevant information. Domestic water demand is influenced both by the number of households and their characteristics, in particular the extent to which they employ water-using appliances. This paper focuses on domestic ownership of water-using appliances in the Republic of Ireland, a country where rapid economic and demographic change have put pressure on water and sewerage infrastructure but where there is little domestic metering. Using a large household micro-dataset, we use regression analysis to examine the determinants of the water and sewage mains connection status of Irish homes and to identify the characteristics of households that are associated with having larger or smaller numbers of appliances. Our empirical results suggest that Ireland will have a rising share of mains water and sewerage connections in th",9.0,https://link.springer.com/content/pdf/10.1007/s11269-010-9583-2.pdf
9ba037c4be8aafd538880b087daec40b7fcdfcc8,10.3390/buildings14061827,Factors Influencing the Usage Frequency of Community Elderly Care Facilities and Their Functional Spaces: A Multilevel Based Study,2024.0,Buildings,Fang Wen; Yan Zhang; Peng-Cheng Du; Ziqi Zhang; Bo Zhang; Yu-Yang Zhang,"The construction of community elderly care facilities (CECF) is pivotal for promoting healthy aging and “aging in place” for older people. This study focuses on the low utilization rates of community elderly care facilities in the Dongcheng and Xicheng Districts, core areas of Beijing. The explainable machine learning method is used to analyze data across three dimensions: the elderly’s individual attributes, characteristics of the community elderly care station (CECS), and features of the built environment around CECS and subdistrict, to identify the important factors that influence the usage frequency of overall CECS and its different functional spaces, and also the correlation between factors and usage frequency of CECS. It shows that the most important factors are the features of CSCF, including the degree of space acceptance and satisfaction with services provided, which influence the usage frequency of nine functional spaces (R2 ≥ 0.68) and overall (R2 = 0.56). In addition, older people’s individual factors, such as age and physical condition, significantly influence the usage of specific spaces such as rehabilitation therapy rooms and assistive bathing rooms. The influence o",9.0,https://doi.org/10.3390/buildings14061827
cb24ed70bb82465f62d9ef9a2f752dd7c8735082,10.3390/su17114926,Explaining Urban Vitality Through Interpretable Machine Learning: A Big Data Approach Using Street View Images and Environmental Factors,2025.0,Sustainability,Dong Li; Houzeng Han; Jian Wang; Xingxing Xiao,"Urban vitality (UV) is a critical indicator for measuring the level of sustainable urban development, closely associated with environmental factors such as population density, economic activity, and spatial utilization efficiency. However, traditional methods face significant limitations in capturing the heterogeneity and nonlinear relationships between urban vitality and its influencing factors. This study suggests an interpretable machine learning framework to address the aforementioned issues. It combines a gradient boosting decision tree (GBDT) model with the SHapley Additive exPlanation (SHAP) framework to examine the urban vitality distribution characteristics and factors that influence them in Beijing’s fifth ring road. The main findings include the following: Urban vitality within Beijing’s fifth ring road exhibits significant spatial clustering and positive correlations, with clear spatial heterogeneity. The plot ratio (PR) exerts a notable positive influence on urban vitality, while green space accessibility (DG) demonstrates the strongest negative impact. The building density (BD), in contrast, shows a strong negative correlation with urban vitality. Variables such as th",9.0,https://www.mdpi.com/2071-1050/17/11/4926/pdf?version=1748353464
5afc56660beb5dbcd761588f9e44dd22b22702db,10.3390/su12187801,Urban Form as a Technological Driver of Carbon Dioxide Emission: A Structural Human Ecology Analysis of Onroad and Residential Sectors in the Conterminous U.S.,2020.0,Sustainability,T. Crawford,"This study investigates the role of urban form as a technological driver of U.S. CO2 emissions for the onroad and residential sectors. The STIRPAT (Stochastic Impacts by Region on Population, Affluence, and Technology) human structural ecology framework is extended by drawing from science and technology studies (STS) to theorize urban form as a sociotechnical system involving practices and knowledge that contribute to urban land use as a material artifact on the landscape influencing emissions. Questions addressed are: (1) “What is the influence of urban form on total sector CO2 emissions?” and (2) “How does the influence of urban form on CO2 emissions differ for metropolitan versus non-metropolitan status?” Spatial error regression models were estimated using county-level CO2 emissions data from Project Vulcan. The National Land Cover Dataset (NLCD) was used to quantify measures of urban form. Other independent variables were derived from U.S. Census data. Results demonstrate carbon reduction benefits achievable through a developed land use mix containing a greater proportion of high intensity relative to low intensity use. Urban form matters, but it matters differently in terms o",9.0,https://www.mdpi.com/2071-1050/12/18/7801/pdf?version=1600692866
f5554f24873eee4a7f96ee512dc0746d5bb87406,10.1177/00420980241259923,Migration across the urban hierarchy: Has China’s urbanisation transitioned from the primate city stage to the secondary city stage?,2024.0,Urban studies,Jun Wen; S. J. Jansen; H. van der Heijden; P. Boelhouwer,"The population density in megacities in China gives rise to challenges, such as traffic congestion and soaring housing prices. A trend of leaving primate cities can be observed as well as a population increase in secondary cities. These trends might point to an urbanisation transition from the primate city stage to the secondary city stage. Research is needed to determine at which stage of urbanisation China currently resides, and who are migrating across the different levels of cities in this stage. In order to answer these questions, the current study combines the theory of differential urbanisation and migrant selectivity, and analyses city-level migration patterns and demographic characteristics of migrants across the urban hierarchy. The findings indicate that China is currently in the intermediate primate city stage, where the upward migration across the urban hierarchy is driven by younger adults with higher education and income, and a lower likelihood of marriage or parenthood. Building upon global evidence, this research further extends the theory of differential urbanisation by incorporating migrant selectivity into the interpretation of urbanisation stages. It reveals th",9.0,https://doi.org/10.1177/00420980241259923
4b326dc94801b2703a3bef85158e9ca150e8d47f,10.1109/punecon55413.2022.10014818,Real Estate Price Prediction using Supervised Learning,2022.0,2022 IEEE Pune Section International Conference (PuneCon),Vedang Matey; Nikita Chauhan; Aditi Mahale; Vidya Bhistannavar; Ajitkumar Shitole,"The least transparent sector of our economy is real estate. Housing prices change daily and are occasionally inflated rather than based on an appraisal. The central focus of our approach is using fundamental factors to forecast house values. Here, we strive to establish our assessments on each essential aspect when deciding the house's price. In our project, three elements affect a house's price: its physical attributes, design, and location. There have been a lot of studies utilizing typical machine learning techniques to estimate house prices effectively. Still, they need to pay more attention to how well each model performs and ignore the less well-known but more sophisticated models. Our project involves predictions using different Regression techniques like Linear Regression, Lasso Regression, and Decision Tree. Our project includes estimating the price of houses without any expectations of market prices and cost increments. The project aims to predict residential prices for customers considering their financial plans and needs. This project means to predict house prices in Pune city with various regression techniques. The project aims to predict cogent housing prices for thos",9.0,
3db2069d924a4b31d68e28b837dcac640ed6fdaa,10.1080/13658816.2025.2456556,Using an attention-based architecture to incorporate context similarity into spatial non-stationarity estimation,2025.0,International Journal of Geographical Information Science,Sen-Sen Wu; Jiale Ding; Ruoxu Wang; Yi-Ge Wang; Ziyu Yin; Bo Huang; Zhen-Hong Du,"Abstract Geographically weighted regression (GWR) facilitates spatial modeling by providing location-specific coefficients to capture spatial non-stationarity. GWR incorporates a distance decay effect, assigning greater weights to proximal observations under the assumption they exert more influence on the regression parameters. However, distant observations may share significant context similarities, such as socioeconomic or environmental factors, which can influence the regression model. This study introduces an attention-based architecture to address context similarity between samples. A deep learning model termed Context-Attention Geographically Weighted Regression (CatGWR) is proposed to integrate context similarity with distance-based proximity to enhance the estimation of spatial non-stationarity in spatial regression models. Such an integration results in contextualized spatial weights for CatGWR to identify the varying patterns of nonstationary relationships across different spatial locations and context conditions. Validation through simulation experiments and an empirical study on housing prices in Shenzhen, China, shows the superior predictive accuracy and robustness of ",9.0,http://hdl.handle.net/10722/367290
e66db3d8e2a91c1ce2f4dbee8390c2d857936f57,10.21837/pmjournal.v17.i9.614,"THE INFLUENCE OF GDP, INTEREST RATE, WAGE, INFLATION AND EXCHANGE RATE ON RESIDENTIAL PROPERTY PRICE IN INDONESIA",2019.0,PLANNING MALAYSIA JOURNAL,Badrud Duja; Heri Supriyanto,"Over the past years, Indonesia’s economic growth has been recorded among the top developing countries. The economic growth is believed to contribute to the increase on residential property prices. The main objective of this study is to analyse the influence of determinants of residential property prices in Indonesia by examining the dynamic relationships of residential property prices reflected through the Residential Property Price Index (RPPI) with Gross Domestic Product (GDP), investment interest rates, wages, inflation and the exchange rate against the US dollar using secondary data over a period of thirteen-years between 2002Q1 and 2014Q4. By applying the Engle-Granger co-integration testand the error correction model, this research aims to see the relationship between the variables both in the short- and long-term. The results of the study indicated that macroeconomic factors that were significantly related to Indonesian residential property prices were GDP, wages, inflation, and exchange rates against the US dollar, while the investment interest rate was not included in these factors. Furthermore, based on the results of the regression analysis on research data, government p",9.0,http://planningmalaysia.org/index.php/pmj/article/download/614/481
489cc44c208a0398d46eed140e9a570dd151786a,10.3846/bmee.2025.22663,How macroeconomic factors impact residential real estate prices in Eastern Europe,2025.0,"Business, Management and Economics Engineering",Laima Okunevičiūtė Neverauskienė; Dominykas Linkevičius; Daiva Andriušaitienė,"Purpose – This study examines how GDP growth, interest rates, and unemployment rates influence residential real estate prices in politically and economically stable Eastern Euro- pean countries, aiming to identify key drivers of property value changes in the region.
Research methodology – The study uses multiple linear regression and Pearson correlation (r) to assess the relationship between variables and housing prices, with ARIMA (3,1,0) applied for short-term price forecasts based on cyclical time series trends.
Findings – The findings show a strong correlation between macroeconomic indicators and residential real estate prices, with the key influencing factor varying by country, reflecting diverse market sensitivities and regional economic contexts.
Research limitations – The research is limited to Eastern European countries with stable political and economic conditions, excluding those facing instability. Future studies could expand the analysis to include such regions to provide a more comprehensive view.
Practical implications – The results provide valuable guidance for policymakers and investors in crafting strategies tailored to specific macroeconomic conditions, enhancing",9.0,https://journals.vilniustech.lt/index.php/BMEE/article/download/22663/12708
e775d81470a5d71c72bb5f38e2642c8d17d10786,10.2478/remav-2013-0019,Spatio-Temporal Analysis of the Real Estate Market Using Geographic Information Systems,2013.0,,P. Cichociński; J. Dąbrowski,"Abstract The paper proposes the use of geographic information system tools for the analysis of spatial and temporal aspects of the real estate market. In particular, it focuses on the graphical presentation of the spatial distribution of price and its variability over time. The possibility of presenting an image of the spatial distribution of prices in the form of a 3D model is studied. A topographic surface is proposed as an alternative to traditional methods of spatial interpolation. Visual verification and numerical comparison have shown its superiority over other previously used methods. The best method of presenting four-dimensional data - the variation in time of the spatial distribution of house prices - was sought. The possibility of taking time into account as one of the attributes of the analyzed and presented objects, available in advanced GIS software, was used for this purpose. The undertaken activities were based on formal guidelines for the registration of time set out in the ISO 19100 series of standards dedicated to geographic information. Potential sources of data for this kind of analysis were identified and their availability was examined. The paper also present",9.0,https://www.sciendo.com/pdf/10.2478/remav-2013-0019
88fc0ba3f17ceb7183aa99b425156679a1a702b4,10.1016/j.ecolecon.2017.11.019,A spatially accurate method for evaluating distributional effects of ecosystem services,2018.0,,A. Fleischer; D. Felsenstein; Michal Lichter,"The value of most ecosystem services invariably slips through national accounts. Even when these values are estimated, they are allocated without any particular spatial referencing. Little is known about the spatial and distributional effects arising from changes in ecosystem service provision. This paper estimates spatial equity in ecosystem services provision using a dedicated data disaggregation algorithm that allocates 'synthetic' socioeconomic attributes to households and with accurate geo-referencing. A GIS-based automated procedure is operationalized for three different ecosystem in Israel. A nonlinear function relates household location to each ecosystem: beaches, urban parks and national parks. Benefit measures are derived by modeling household consumer surplus as a function of socio-economic attributes and distance from the ecosystem. These aggregate measures are spatially disaggregated to households. Results show that restraining access to beaches causes a greater reduction in welfare than restraining access to a park. Progressively, high income households lose relatively more in welfare terms than low income households from such action. This outcome is reversed when dis",9.0,http://ageconsearch.umn.edu/record/264872
5b4eeb84872df0e74f70c51edf16a3625e0dc2cc,10.1108/ijse-11-2019-0699,Spatial disparity and inequality of education domain in Algeria: a spatial econometric approach,2020.0,,Mohammed Touitou; Laib Yacine; Boudeghdegh Ahmed,"PurposeDespite significant progress in schooling, social and spatial inequalities in access to education remain important in Algeria. In the present article, taking into account the geographic dimension makes it possible to identify the links existing between spatial location and disparities in the field of education in Algeria. Also, three types of education indicators (quantity, quality and inequality) are used in the study. The study’s sample includes 48 Algerian provinces, studied between 2008 and 2018.Design/methodology/approachIn this study, the authors used data from the 2008 and 2018 General Census of Population and Housing (GCPH) for 48 provinces. Indeed, the two censuses of 2008 and 2018 (sources of data for this study) were based on questionnaires intended for different categories of the population (households, non-household populations, transit population, etc.). Therefore, the no response rate is assumed to be close to 0. Using spatial econometric techniques.FindingsResults indicate that the indicator used is strong spatial disparity in education in Algeria. The development of a spatial synthetic index (SI) makes it possible to measure more precisely the extent and nat",9.0,
0a83035a3bf295ef93d94e0efe27dbad7fa6772a,10.1109/tsp.2024.3442858,Bayesian Deep Learning via Expectation Maximization and Turbo Deep Approximate Message Passing,2024.0,IEEE Transactions on Signal Processing,Wei Xu; An Liu; Yiting Zhang; Vincent K. N. Lau,"Efficient learning and model compression algorithm for deep neural network (DNN) is a key workhorse behind the rise of deep learning (DL). In this work, we propose a message passing-based Bayesian deep learning algorithm called EM-TDAMP to avoid the drawbacks of traditional stochastic gradient descent (SGD)-based learning algorithms and regularization-based model compression methods. Specifically, we formulate the problem of DNN learning and compression as a sparse Bayesian inference problem, in which group sparse prior is employed to achieve structured model compression. Then, we propose an expectation maximization (EM) framework to estimate posterior distributions for parameters (E-step) and update hyperparameters (M-step), where the E-step is realized by a newly proposed turbo deep approximate message passing (TDAMP) algorithm. We further extend the EM-TDAMP and propose a novel Bayesian federated learning framework, in which the clients perform TDAMP to efficiently calculate the local posterior distributions based on the local data, and the central server first aggregates the local posterior distributions to update the global posterior distributions and then update hyperparamete",9.0,https://arxiv.org/pdf/2402.07366
cc51ad02599152e7b283c614e157d51386278ab8,10.1080/23754931.2021.1958251,Voter Turnout in Portugal: A Geographical Perspective,2020.0,Papers in applied geography,L. Manoel; A. Costa; P. Cabral,"Abstract The decline of voter turnout in Portugal was confirmed in the legislative election of 2015. The unquestionable democratic value associated with the act of voting, leads to the discussion of this issue, and emphasizes the need for additional investigation. Particularly, it is crucial to identify the characteristics of citizens who vote, to better understand the phenomenon and think about solutions. This work identified the most significant sociodemographic variables in explaining voter turnout in continental Portugal and described the relationship between those variables and voter turnout, including the geographical variation existing across the municipalities. A Semiparametric Geographically Weighted Regression (SGWR) model enabled the investigation of local variations in turnout values, simultaneously considering that its relationship with some variables might vary over space. Results show that turnout is influenced by a set of sociodemographic variables. While some variables affect turnout differently over the country (percentage of family cores with children aged less than 15, and percentage of owner-occupied houses), others affect it uniformly (percentage of graduated ",9.0,https://run.unl.pt/bitstream/10362/146026/1/Voter_Turnout_in_Portugal_geographical_perspective.pdf
c9cc5e2b62208ccef2371e510d60b6be6cb05a3d,10.3390/ijerph182212195,How Does Air Pollution Influence Housing Prices in the Bay Area?,2021.0,International Journal of Environmental Research and Public Health,Minmeng Tang; D. Niemeier,"In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO2, and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral’s I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air con",9.0,https://www.mdpi.com/1660-4601/18/22/12195/pdf?version=1637641875
bd42cea5779904b39c488f5e59235573696a2827,10.3846/ijspm.2022.15971,THE IMPACT OF URBAN RENEWAL ON NEIGHBORING HOUSING PRICES: AN APPLICATION OF HIERARCHICAL LINEAR MODELING,2022.0,International Journal of Strategic Property Management,Chun-Chang Lee; Chih-Min Liang; Wen-Chih Yeh; Zheng Yu,"This study explored the impacts of urban renewal projects on neighboring housing prices. Hierarchical linear modeling (HLM) was employed to analyze urban renewal projects in Taipei City. The Level 1 independent variables pertained to a house itself (19,157 pieces of data), such as its structure and neighborhood attributes. The Level 2 variable pertained to an urban renewal project (23 cases of urban renewal), and the explanatory variable was the scale of each urban renewal project. The study examined whether differences exist between the impacts of various urban renewal projects on neighboring housing prices, and analyzed the extent to which the differences in neighboring housing prices are caused by the differences between urban renewal projects. The empirical results showed that the mean housing price varies significantly between each urban renewal project. In regard to the variance in the mean house price, 31.46% was caused by the differences between the urban renewal projects. The estimated coefficient of the grand floor area of urban renewal (FLAREA) had a positive value and attained a 1% level of significance. This indicates that the larger the scale of an urban renewal proje",9.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/15971/10908
4b3ca52746bb0b6b7421cf2835338f53acd9b559,10.1080/12265934.2024.2382707,Exploring the spatio-temporal driving mechanism of multi-dimensional urbanization on urban residential buildings based on GTWR: a case study of China,2024.0,International Journal of Urban Sciences,Wei-Min Xiang; Yeqiang Lan; Lei Gan,"ABSTRACT Examining how urbanization affects carbon emissions in the construction industry is crucial for China's efforts to achieve environmentally friendly and low-carbon growth. Nevertheless, current research predominantly employs a unidimensional approach to gauge urbanization, overlooking the nuanced characteristics of novel urbanization. This study explored the spatial and temporal characteristics of the impact of urbanization on carbon emissions from urban residential buildings from a multidimensional perspective, starting from five dimensions: population, economy, land, life, and ecology, using a spatial–temporal geographically weighted regression model (GTWR) and panel data from 30 provinces in China for the period 2010–2020. The findings of this study revealed that, on the whole, population urbanization, land urbanization, and lifestyle urbanization exert a significant facilitating influence on carbon emissions from urban residential buildings. Conversely, economic urbanization exhibits a notable inhibitory effect on carbon emissions from urban residential buildings. Furthermore, ecological urbanization transitions from an inhibitory effect to a promoting effect on carbon ",9.0,
7491cbc3adc6251858a7a6927a72d8a75befe625,10.3390/ijgi12070298,"The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia",2023.0,ISPRS Int. J. Geo Inf.,Yi Lu; Vivien Shi; C. Pettit,"Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this gap, three types of valuation models were applied to sales data from both non-strata and strata properties, and how the proximity and quality of public schools have influenced the prices of different residential property types was examined. The findings demonstrate that an increase of one unit in the normalised NAPLAN score of primary and high schools will lead to a 3.9% and 1.4%, 2.7% and 2.8% rise in housing prices for non-strata and strata properties, respectively. It is also indicated that the application of geographically weighted regression (GWR) can better capture the varying effects of schools across space. Moreover, properties located in the catchment of high-scoring schools in northern Greater Sydney are consistently the most influenced by school quality, regardless of the property type. These findings contribute to a comprehensive understanding of the rel",9.0,https://www.mdpi.com/2220-9964/12/7/298/pdf?version=1690200021
ba3f01884c3d8317f3e539af4966d2cc6b6802aa,10.1016/j.neunet.2025.107364,Deep Huber quantile regression networks,2023.0,Neural Networks,Hristos Tyralis; G. Papacharalampous; N. Dogulu; Kwok-Pan Chun,"Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issuing predictions of more functionals of the predictive probability distribution (quantiles and expectiles) has been recognized as a means to quantify the uncertainty of the prediction. In deep learning (DL) applications, that is possible through quantile and expectile regression neural networks (QRNN and ERNN respectively). Here we introduce deep Huber quantile regression networks (DHQRN) that nest QRNN and ERNN as edge cases. DHQRN can predict Huber quantiles, which are more general functionals in the sense that they nest quantiles and expectiles as limiting cases. The main idea is to train a DL algorithm with the Huber quantile scoring function, which is consistent for the Huber quantile functional. As a proof of concept, DHQRN are applied to predict house prices in Melbourne, Australia and Boston, United States (US). In this context, predictive performances of three DL architectures are discussed along with evidential interpretation of results from two economic cas",9.0,https://doi.org/10.1016/j.neunet.2025.107364
43f3853b304499d60812c9740e883b4b9619dd77,10.3390/jrfm15120560,"Housing Affordability, Public Policy and Economic Dynamics: An Analysis of the City of Lisbon",2022.0,Journal of Risk and Financial Management,Miguel Lorga; João Fragoso Januário; C. Cruz,"The increasing growth of population living in cities, associated with the commoditization of investment in real estate, has impacted real estate prices and created obstacles for average income families to meet their housing needs. This problem is generalized to virtually all cities, but it has assumed larger proportions in cities where economic activities (tourism, financial services, high-tech industry) have flourished after the financial crisis. Lisbon is one of those cases. The growth of short-term rentals led to an increase in the property prices well above the average income growth, eroding housing affordability. This paper will focus on analyzing Lisbon´s affordability and understanding its main determinants. The analysis is carried out from the compilation and processing of data from 2004 to 2019, in the context of the municipality of Lisbon, using statistical instruments of linear regression in an exploratory and predictive approach. The results suggest a great influence of factors such as tourism, the foreign population with resident status, the propagation of short-term rentals and public policies on the worsening of housing affordability. In view of these conclusions, th",9.0,https://www.mdpi.com/1911-8074/15/12/560/pdf?version=1669886008
942b29d7b4c0cda490dd0130537d1b619f1f7e2b,10.1108/ijhma-11-2021-0122,Foreign capital inflow and housing prices in a developing country: an advanced econometric enquiry,2022.0,International Journal of Housing Markets and Analysis,Rafiq Ahmed; Syed Tehseen Jawaid,"
Purpose
The study is intended to find out the relationship between housing prices and the inflow of foreign capital in Pakistan. There is a shortage of housing units due to rising population and rural–urban migration since its inception; on the other hand, there is also a lack of housing finances. The urban sprawl has created the demand for housing units, but the supply of housing has not been increased up to the required level, the major reason is a deficiency of housing finances.


Design/methodology/approach
The analysis was carried out from 1973 to 2018, on an annual, quarterly and monthly basis; the structural changes are captured by the Zivot–Andrews unit root test. Gregory–Hansen test is used for cointegration, the combined cointegration also validates the results. In addition, the rolling window is used to capture timely changes between data sets. Finally, wavelet analysis is used to prove volatility.


Findings
The rising prices of housing in the country is alarming; Pakistan is a developing country, and it is facing many problems along with a housing shortage. The domestic sources of housing finances are inadequate, so foreign funds are welcomed. The rolling window regre",9.0,
36558a01c7d6e6920e060c56ce4ab51416b9c581,10.21837/pm.v21i26.1270,THE EFFECTS OF COVID-19 ON RESIDENTIAL PROPERTY PRICES IN MALAYSIA,2023.0,PLANNING MALAYSIA,Siti Hafsah Zulkarnain; Abdol Samad Nawi; A. MohdAini,"The physical distancing, lockdown, and other measures due to the COVID-19 pandemic have caused massive job loss and economic hardship, which would result in people not being able to pay their mortgages or rent.   Thus, the main objective of this study is to examine the effects of the Gross Domestic Product (GDP), inflation, investment, and interest rate on residential property prices in Malaysia before and during COVID-19. This research utilises the annual time series data from 1990 to 2020, and the empirical analysis is conducted using the multiple linear regression analysis modelling approach. The finding shows that GDP and interest rates are positive and statistically significant with residential property prices. Practitioners can use these findings to understand the pandemic's effect on house prices. This will provide some guidelines for a policy formulation to moderate the increase in residential property prices to maintain the country's economic stability and indirectly help the government realise its vision of becoming a high-income country between 2024 and 2028. This is the first study that examines the pricing behaviour of Malaysian house prices concerning the COVID-19 sho",9.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1270/951
52c671b9aec7face27a100b76e8445629de70e8f,10.3390/su14127496,Housing Sustainability: The Effects of Speculation and Property Taxes on House Prices within and beyond the Jurisdiction,2022.0,Sustainability,M. A. Rauf; O. Weber,"Housing plays an essential role in sustainable governance due to its socio-economic and environmental connection. However, the relationship between governance policies, market behavior, and socio-economic outcomes varies geographically and demographically. Therefore, segregated policies developed and implemented may fail to achieve their desired objectives because of the sensitivity of housing policies for their connection to human wellbeing. The effectiveness of housing policies in geographically connected regions is one of the areas that has received little attention in the Canadian context. The study follows a multi-step empirical method using a multiple linear regression model and a difference-in-difference approach to assessing the geographical variation of speculation and property taxes on housing markets. The study confirms that speculation taxes are not an effective tool in curbing house prices. Similarly, considering the role of property taxes in providing public services, delinking property taxes from a potential contributor to house prices would provide a better lens to develop local housing policies. Furthermore, the study also confirms that the housing market can be be",9.0,https://www.mdpi.com/2071-1050/14/12/7496/pdf?version=1655721262
a05d895f70d786ddd264329b2583cf32765d3ee0,10.1016/j.athoracsur.2023.01.002,Neighborhood Socioeconomic Status Independently Predicts Outcomes After Mitral Valve Surgery.,2023.0,Annals of Thoracic Surgery,A. Iyengar; W. Patrick; M. Helmers; J. Kelly; Jason J. Han; Matthew L. Williams; Emily J. MacKay; Nimesh D. Desai; M. Cevasco,"BACKGROUND
Socioeconomic status has increasingly recognized influence on outcomes after cardiac surgery. However, singular metrics fail to fully capture the socioeconomic context within which patients live, which vary greatly between neighborhoods. We sought to explore the impact of neighborhood-level socioeconomic status on patients undergoing mitral valve surgery in the US.


METHODS
Adults undergoing first-time, isolated mitral valve surgery were queried from the STS Adult Cardiac Surgery Database between 2012-2018. Socioeconomic status was quantified utilizing the Area Deprivation Index, a weighted composite including average housing prices, household incomes, education, and employment levels. The associations between regional deprivation, access to mitral surgery, valve repair rates, and outcomes were evaluated using logistic regression. .


RESULTS
Among 137,100 patients included, patients with socioeconomic deprivation had less elective presentations, more comorbidity burden and more urgent/emergent surgery. Patients from less disadvantaged areas received operations from higher volume surgeons and had higher repair rates (highest vs. lowest quintile: 72% vs. 51%, p<0.001, mo",9.0,
08cae2a1e16a589328926c4a643ccf18a6e5c8a0,10.1016/j.wasman.2025.115026,"Spatiotemporal evolution, driving factors and generation patterns of construction and demolition waste: A multi-scale analysis based on provinces and cities in China.",2025.0,Waste Management,Yiwei Chen; Qiu Xie; Yingbin Feng; Yuxin Huang; Yi Yang; Tong Zhang,"Construction and demolition waste generation (CDWG) reduction is a key issue in urban waste management. The complex distribution and regional disparities of CDWG exacerbate the challenges of reduction. Existing research mainly focuses on macro-level national or regional scales, neglecting multi-scale interactions and detailed analysis at city scale. To address this gap, this study proposes a multi-scale STDP framework to systematically analyze the spatiotemporal evolution, driving mechanisms, and generation patterns of CDWG. Using China as a case and applying spatial econometrics and machine learning models, the main findings are as follows: (1) CDWG distribution consistent with Hu-Line, and the gap between two sides of the line gradually widened; (2) CDWG consistently shows spatial autocorrelation, with an ""increase-decrease-recovery"" fluctuation in aggregation, and city-scale correlation is always lower than at provincial scale; (3) Construction industry development and neighborhood influence are key drivers at provincial scale, while real estate market activity, housing demand, and urban economic level are major drivers at city level. Additionally, CDWG generation patterns for 3",9.0,
b8855455c72c3a07ee1c1902b2450e50e4a0ab90,10.1016/j.socscimed.2021.113868,An ecological study of the relationship between urban built environment and cardiovascular hospital admissions (2004-2016) in an Asian developing country.,2021.0,Social Science & Medicine (1967),Soo-Chen Kwan; Rohaida Ismail; N. Ismail; N. Mohamed,"This study aims to evaluate the relationship between urban built environment and hospital admissions from cardiovascular diseases in Kuala Lumpur, Malaysia. Hospital admission data from 2004 to 2016 for cardiovascular diseases were used with patient residential postcodes as the unit of analysis. Data was split into 2004-2009 (12,551 cases) and 2010-2016 (17,154 cases) periods corresponding to land use data. We used generalized linear mixed model to analyse population density, property value, entropy index, and the kernel density (800 m) of specific land use, bus and rail stations, and road junctions, with time period and postcodes as the random effects to generate incidence rate ratios (IRRs). Results indicated that entropy index and recreational area density were associated with fewer hypertensive disease and ischemic heart disease hospital admissions (IRR range: 0.49-0.68, 95%CI: 0.27, 0.97). Population density and property value were associated with fewer cerebrovascular disease hospital admissions (IRR range: 0.29-0.34, 95%CI: 0.11, 0.75). Contrarily, density of road junctions was associated with 2.5-6.3 times more hospital admissions for cardiovascular disease hospital admissi",9.0,
f3a4adb60cf58a077f8978abbbf5d344a356a194,10.3390/bdcc6030096,Improving Real Estate Rental Estimations with Visual Data,2022.0,Big Data and Cognitive Computing,Ilia Azizi; Iegor Rudnytskyi,"Multi-modal data are widely available for online real estate listings. Announcements can contain various forms of data, including visual data and unstructured textual descriptions. Nonetheless, many traditional real estate pricing models rely solely on well-structured tabular features. This work investigates whether it is possible to improve the performance of the pricing model using additional unstructured data, namely images of the property and satellite images. We compare four models based on the type of input data they use: (1) tabular data only, (2) tabular data and property images, (3) tabular data and satellite images, and (4) tabular data and a combination of property and satellite images. In a supervised context, the branches of dedicated neural networks for each data type are fused (concatenated) to predict log rental prices. The novel dataset devised for the study (SRED) consists of 11,105 flat rentals advertised over the internet in Switzerland. The results reveal that using all three sources of data generally outperforms machine learning models built on only tabular information. The findings pave the way for further research on integrating other non-structured inputs, ",9.0,https://www.mdpi.com/2504-2289/6/3/96/pdf?version=1663293542
9a69793c87e7c893665ba6b7390e4e37f897dcae,10.2298/pan1004405g,“The Price You Pay”: The Impact of State-Funded Secondary School Performance on Residential Property Values in England,2010.0,,J. Glen; J. Nellis,"This paper examines the relationship between state-funded secondary school performance and local residential property values in seven major English cities. When choosing which secondary school they wish their children to attend, parents will be aware of the school’s performance in Key Stage 3, GCSE and A- level examinations. We suggest that GCSE examination results will be the measure of school performance that parental choice will be most closely correlated with. Therefore, secondary schools with good GCSE examination results will be “oversubscribed” in that more students will wish to attend these schools than there are places available. Schools will then have to develop mechanisms for rationing the available places - central to rationing strategies in English schools at the moment is geographical proximity of the family home to the school of choice. Parents will thus have a strong incentive to purchase houses in the “catchment” area of high performing schools. Our results suggest that this is the case, with high performing schools stimulating a price premium in local residential property markets of between 1% and 3% for each additional 10% point improvement in the pass rate in GC",9.0,http://www.doiserbia.nb.rs/ft.aspx?id=1452-595X1004405G
bfe997b98e3542a49360bc452f73954be5d5aaca,10.1111/geoj.12610,The determinants of double energy vulnerability: A geospatial analysis,2024.0,Geography Journal,S. Bouzarovski; Paul Mckenzie; Aileen Lonie; Amish Sarpotdar; Giulio Mattioli; Mari Martiskainen,"This paper examines the spatial and social differences in people's lack of access to adequate energy and transport services in the UK. We respond to the need for developing a differentiated understanding of both the drivers and expressions of this ‘double energy vulnerability’ (DEV), while seeking to integrate and analyse relevant information from all four UK nations. Using a variety of statistically representative census and survey datasets, the paper develops a series of multi‐dimensional indices to map transport‐ and energy‐related injustices. This is followed by a cluster analysis to examine broad‐level geographical patterns, and a Geographically Weighted Regression (GWR) model to explore the spatial variation of vulnerabilities related to contingencies such as income, ethnicity and housing. The paper corroborates the results of previous qualitative studies, and research within selected UK nations, while revealing several unexpected territorial clusters and underpinnings of infrastructural injustice. DEV is shown to disproportionately affect coastal, highland, peripheral and rural regions, with an internal granularity that exhibits high levels of variation within urban and peri",9.0,https://doi.org/10.1111/geoj.12610
5242a8699dbfd95d3c698daaa18f7bab00d59c97,10.3846/ijspm.2020.13649,THE IMPACT OF LUXURY HOUSING ON NEIGHBORHOOD HOUSING PRICES: AN APPLICATION OF THE SPATIAL DIFFERENCE-IN-DIFFERENCES METHOD,2020.0,,Chih-Min Liang; Chun-Chang Lee; You-Hsin Lin; Zheng Yu; Wen-Chih Yeh,"This study investigated the spatial spillover effects of luxury housing during and after construction, in regards to increases in housing prices in neighboring areas as well as the spatial dependence of neighboring housing. This study focused on already completed luxury housing in Taipei, Taiwan. First, the nearest-neighbor matching approach of propensity score matching was used to overcome the problem of data heterogeneity. The difference-in-differences (DD) method and spatial econometrics were used for analysis. The empirical results indicated that the spatial error model had the best goodness of fit. This indicated that housing prices increased by 13.0% during construction of luxury housing nearby. This indicated that housing prices increased by 5.8% after the construction of luxury housing nearby. The empirical results showed that the ongoing and completed construction of luxury housing had spillover effects on housing prices. The effect of ongoing construction of luxury housing was particularly large in scope, indicating its role as a predictor of psychological reaction in the market.",9.0,https://journals.vgtu.lt/index.php/IJSPM/article/download/13649/10173
c622399445897b90cb3517819c06ed20d29092a4,10.1017/s1742058x22000236,Racial Discrimination and Economic Factors in Redlining of Ohio Neighborhoods,2022.0,Du Bois Review: Social Science Research on Race,A. Perzynski; Kristen A. Berg; Charles B. Thomas; A. Cemballi; T. Smith; Sarah Shick; D. Gunzler; A. Sehgal,"Abstract We examined the influence of racial and ethnic identity of residents and housing market economic conditions on redlining. Data were extracted from archival area description forms from the Home Owners’ Loan Corporation for 568 Ohio neighborhoods from 1934–1940. Logistic regression analysis was used to analyze the relationships between neighborhood characteristics and redlining. Bivariate results indicated a strong association between the presence of African American residents and neighborhood redlining (OR = 40.9, 95% CI 22.9-72.8). Multivariable analysis demonstrated that some neighborhood characteristics were contributors to the decision to redline, including homes in poor condition (OR = 4.3, 95% CI 1.2-15.1), home vacancy (OR = 1.4, 95% CI 1.1-1.6), and housing prices (per thousand dollars) (OR = 0.7, 95% CI 0.4-1.2). Adjusting for these and other factors, the presence of African American residents remained a powerful predictor of redlining (OR = 13.8, 95% CI 4.4-42.8). Racial discrimination was the overriding factor in decisions to redline neighborhoods.",9.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/5CC7A240CF3EDE2267DCC3836EE5C377/S1742058X22000236a.pdf/div-class-title-racial-discrimination-and-economic-factors-in-redlining-of-ohio-neighborhoods-div.pdf
cedcbf4dc68b72010d51562d21f1a97155deb882,10.1088/1757-899x/1022/1/012110,Empirical analysis of regression techniques by house price and salary prediction,2021.0,IOP Conference Series: Materials Science and Engineering,U. Bansal; A. Narang; A. Sachdeva; I. Kashyap; S. Panda,"Regression analysis is extensively used for prediction and prognostication, and its use has substantial overlap with the domain of machine learning. The main objective of this paper is to compare the performance of two regression techniques namely Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) algorithms by two cases: predicting the salary of employees after certain years and predicting the prices of real estates. An employee’s salary depends on numerous factors, such as total employee experience, certifications, and overall experience as a lead and manager. The factors in predicting house prices are the area of land (sqft_living), condition, waterfront, number of bedrooms, and so on. The dataset used in this experiment is an open-source dataset from KaggleInc. The algorithms were compared using parameters like R-squared value, Mean absolute error (MAE), Mean Squared Error (MSE), Median Absolute Error (MDAE), Variance Score, and Root Mean Square Error (RMSE). Results have shown that MLR provides the better efficiency in comparison to SLR.",9.0,https://iopscience.iop.org/article/10.1088/1757-899X/1022/1/012110/pdf
e398442e199c9e9e1791973ecf9d2344e257a743,10.1371/journal.pone.0280235,Spillover effect analysis of home-purchase limit policy on housing prices in large and medium-sized cities: Evidence from China,2023.0,PLoS ONE,Guan-Cen Wu; Wenjing Guo; Xing Niu,"Home-purchase limit is a unique administrative housing policy of China and has non-negligible influences on the housing price. The objective of this study is to analyze the spillover effect of home-purchase limit policy on housing prices in 35 large and medium-sized cities. The panel data of these cities and the spatial Durbin model are employed in this study. The results indicate that the spillover effect of home-purchase limit policy is positive and significant in all of 35 cities. However, when we categorize these cities into high-risk, medium-risk, and low-risk based on housing price characteristics, the spillover effect of home-purchase limit policy is different. It is not significant in high-risk cities, is negatively significant in medium-risk cities, and is positively significant in low-risk cities. This paper suggests that local governments can pay more attention to the precise design and implementation of home-purchase limit policy, and maintain policy continuity to avoid further spillover fluctuations in housing prices.",9.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0280235&type=printable
9c47e3514edca37de0878f6eaae6d2cdd7907fd3,10.3390/e20110831,Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach,2018.0,Entropy,O. Omer,"In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.",9.0,https://doi.org/10.3390/e20110831
419e5bf2d7fa49b54398502c22399d6e2f1a9e98,10.1108/17538331011083934,Satisfaction with neighbors and neighborhood housing prices,2010.0,,J. Blair; J. E. Larsen,"Purpose – The purpose of this study is to test the hypothesis that neighborhoods characterized by satisfying social relationships among residents have higher housing prices than areas where people are less satisfied with their neighbors.Design/methodology/approach – A semi‐logarithm regression model is used to test whether the extent of satisfaction with neighbors is significantly related to transaction prices of houses in 59 neighborhoods in Dayton, Ohio.Findings – The results are consistent with, and more specific than, previous studies linking social capital to neighborhood stabilization. Resident satisfaction with their neighbors is found to be an important determinant of property value controlling for housing characteristics.Practical implications – The findings support stabilization and economic development strategies that seek to enhance social relationships in urban neighborhoods.Originality/value – This study is the first effort to examine the impact of relations among neighbors on housing prices...",9.0,https://works.bepress.com/james-larsen/28
4b2e41c7138ea7ac90042f290d72f1122fbd0bfd,10.1080/13923730.2006.9636401,Analysis of the dynamics of walking distances to public transport routes and its influence on housing prices,2006.0,,R. Ušpalytė-Vitkūnienė; M. Burinskienė,"Abstract Accessible public transport system is an essential indicator that guarantees equal travelling opportunities for all people. Scientific research has revealed that poor accessibility to public transport mostly affects low‐income people and the elderly. People from these groups have fewer opportunities to reach education institutions, working places, or health institutions. Access to public transport routes is among public transport indicators that are important for these social groups of people. An analysis reveals that during the last six years, from 1998 to 2004, access to public transport increased on average by 1,28 % in the district area. The analysed indicators of population density, working density, ratio of built up territories and green areas, accessibility of the public transport, density of routes in the area in question, and the distance to the centre revealed that the accessibility to public transport also has a significant influence on the price of housing, especifically for the above...",9.0,https://journals.vgtu.lt/index.php/JCEM/article/download/8811/7690
43fd23f8f138892de1b7069a35513a539b2133e8,10.1108/ijhma-12-2020-0147,An analysis of the distribution and price determinants of Airbnb rentals in Malta,2021.0,,Rafael Fearne,"
Purpose
The purpose of this paper is twofold: to explore the distribution and pricing characteristics of Airbnb listings in Malta as at May 2019; and to develop a pricing model to determine the factors which have a statistically significant impact on price per night of listings.


Design/methodology/approach
A descriptive analysis of location and pricing of listings was undertaken via heat mapping techniques. A cross-sectional ordinary least squares (OLS) regression was run to determine the statistically significant variables.


Findings
Listings tend to cluster around not only in traditional tourist towns but also in rural areas which opens up new opportunities for tourist lodging. The Southern Harbour region was found to be the most expensive with the Gozo and Comino region being the least expensive. The coefficients of the pricing regression model were in line with a priori expectations.


Research limitations/implications
The study is based on a cross-sectional data set and thus fails to account for seasonal changes in prices. Likewise, the use of an OLS regression without incorporating quantile regression methods or spatial autocorrelation econometric techniques is another li",9.0,
bd410d68ec0e8b7d23c82ab0b12a911c45826410,10.1108/jerer-03-2019-0006,An early warning system to identify house price bubbles,2019.0,Journal of European Real Estate Research,D. Hagemann; Monika Wohlmann,"
Purpose
The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles.


Design/methodology/approach
The results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables.


Findings
The empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland.


Practical implications
The results implicate a new global housing boom, particularly in those countries that did not experience a major price correc",9.0,
5759df8e6586c8383ba24bc050a6d4d22579b00e,10.18488/journal.107.2014.24.61.71,"An Empirical Study of the Impact of Metro Station Proximity on Property Value in The Case of Nanjing, China",2014.0,Asian Development Policy Review,Xinyue Zhang; Yan-Qing Jiang,"This paper analyzes the impact of metro station proximity on property value from both theoretical and empirical perspectives. Data on second-hand apartments within six kilometers of metro line 1 and 2 stations in Nanjing are used. Our empirical results show that metro station proximity has a positive partial effect on property value. The degree of impact diminishes with the increase of the distance. The extent of impact reaches the largest when the distance is less than five hundred meters, and is still remarkable when the distance is within two kilometers. The effect becomes insignificant when the distance is larger than two kilometers. Our empirical results also show that with the distance increasing, the extent of price variation is widened first, reaching a peak when the property is within one kilometer, and then decreases. In addition, metro station proximity in suburban areas is shown to have a higher positive impact on property value compared with metro station proximity in urban areas.",9.0,https://archive.aessweb.com/index.php/5008/article/download/409/763
2794b8fa32fc6a91b1a93265f6339ef257dc0981,10.1155/2022/3590224,Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model,2022.0,Computational Intelligence and Neuroscience,Xin-Shu Li,"In order to improve the reliability of housing price prediction and analysis, this article combines the generalized linear regression model to build a real estate price prediction model and analyzes the basic knowledge of data mining. On the basis of this prior knowledge, this article investigates the cluster analysis algorithm and selects the generalized linear regression model as the research focus based on its definition and the characteristics of stock data. Moreover, this article analyzes the estimation methods of the generalized linear regression model and the nonparametric regression model, and then gives the estimation method of a partial linear model. In addition, this article verifies the validity of the model proposed in this article by means of simulation research. Through the simulation and comparison experiments, it can be seen that the housing price prediction system based on the generalized regression model proposed in this article has a high housing price prediction accuracy.",9.0,https://downloads.hindawi.com/journals/cin/2022/3590224.pdf
b46f39db3e5093713cd13e6f7a0ec75031945aef,10.54097/hset.v49i.8521,Comparison of Real Estate Price Prediction Based on LSTM and LGBM,2023.0,Highlights in Science Engineering and Technology,Song-Yi Shi,"Housing price prediction is one of the widely discussed topics, and the application of machine learning and deep learning models to housing price prediction is a research hotspot. Exploring which models are suitable for predicting real housing price datasets has significant implications for guiding government and homebuyers' decision-making. In this study, the LSTM and LightGBM models were selected as research objects, and the suitability of the models was explored and compared using the second-hand housing price dataset in Beijing. Based on the analysis, in the task of pure time series prediction based on historical housing prices, the LSTM model had a better fit (R2=0.91), while in the task of housing price prediction based on multiple influencing factors, the LightGBM model had better comprehensive evaluation index results (R2=0.53). Both models can be used to predict pure time series, but the LSTM model is not suitable for predicting multi-factor input models that are not time series.",9.0,https://drpress.org/ojs/index.php/HSET/article/download/8521/8294
ec795a04dfe00b707ee241bc52f433835ef5677c,10.1108/ijhma-08-2020-0095,Modelling the affecting factors of housing price using GMDH-type artificial neural networks in Isfahan city of Iran,2021.0,,Behrooz Nazemi; Mohsen Rafiean,"
Purpose
The purpose of this paper is to use Group Method of Data Handling (GMDH)-type artificial neural network to model the affecting factors of housing price in Isfahan city housing market.


Design/methodology/approach
This paper presents an accurate model based on GMDH approach to describing connection between housing price and considered affecting factors in case study of Isfahan city based on trusted data that have been collected from 1995 to 2017 for every six months. The accuracy of the model has been evaluated by mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) in this case.


Findings
Due to the obtained value of MAPE, RMSE and MAE and also their interpretation, accuracy of modelling the factors affecting housing price in Isfahan city housing market using GMDH-type artificial neural network that has been conducted in this paper, is acceptable.


Research limitations/implications
Due to limitation of reliable data availability about affecting factors, selected period is from 1995 to 2017. Accessing to longer periods of reliable data can improve the accuracy of the model.


Originality/value
The key point of this research i",9.0,
a2f160f1c364608adf362346567e4a1a3c71ad3c,10.2139/ssrn.2592476,Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach,2016.0,,S. Leorato; M. Mezzetti,"A hierarchical Bayesian model for spatial panel data is proposed. The idea behind the proposed method is to analyze spatially dependent panel data by means of a separable covariance matrix. Let us indicate the observations as yit, i = 1, ... ,N regions and t = 1,... , T time, var(y), the covariance matrix of y is written as a Kronecker product of a purely spatial and a purely temporal covariance. On the one hand, the structure of separable covariances dramatically reduces the number of parameters, while on the other, the lack of a structured pattern for spatial and temporal covariances permits to capture possible unknown dependencies (both in time and space). The use of the Bayesian approach allows to overcome some of the difficulties of the classical (MLE or GMM based) approach. We present two illustrative examples: the estimation of cigarette price elasticity and of the determinants of the house price in 120 municipalities in the Province of Rome.",9.0,https://doi.org/10.2139/ssrn.2592476
8038f276c31d6d9717f8575fbb01ebaddf844070,10.2139/ssrn.4280134,Navigating the Housing Channel of Monetary Policy Across Euro Area Regions,2024.0,Social Science Research Network,Niccolò Battistini; Matteo Falagiarda; Angelina Hackmann; M. Roma,"This analysis assesses the role of the housing market in the transmission of conventional and unconventional monetary policy across euro area regions. By exploiting a novel regional dataset, we show that monetary policy propagates effectively to economic activity and house prices, albeit in a heterogeneous fashion across regions. Although the housing channel plays a minor role in the transmission of monetary policy to the economy on average, its importance increases in the case of unconventional monetary policy. In addition, we find that unconventional monetary policy has been a key driver of economic activity and house prices since 2013. We also explore the determinants of the diverse transmission of monetary policy to economic activity across regions, finding a larger impact in areas with lower labour income. An expansionary monetary policy can thus be effective in mitigating regional inequality via its stimulus to the economy.",9.0,https://doi.org/10.2139/ssrn.4280134
f5da13a7d6d9928b1913b5d51d882fd604297e6d,10.3389/fpsyg.2019.01618,Moderation of Housing-Related Factors on Psychological Capital–Job Embeddedness Association,2019.0,Frontiers in Psychology,Tian-Zhou Ren; Xi-Zan Jin; Muhammad Rafiq; Ta-Chia Chin,"This study identified three critical housing-related factors (HRFs) as moderators of the relationships between employees’ psychological capital (PsyCap) and job embeddedness (JE) in China’s entrepreneurial environment. The hypotheses were tested with multiple hierarchical regression modeling, using the data collected from 312 employees in manufacturing organizations. The results demonstrated that HRFs (i.e., home ownership, housing price satisfaction, and contributions to housing funds) moderate the relationships between employees’ PsyCap and JE. Specifically, these relationships were stronger when the HRFs were high. Such results contribute to the PsyCap and JE literature by incorporating housing as an extrinsic life-aspect factor that might affect employees’ psychological state and thus their retention in works. Implications for the organization policies and directions for future studies were discussed.",9.0,https://www.frontiersin.org/articles/10.3389/fpsyg.2019.01618/pdf
bdb088b5ce07dbdf3c948091c16610e20274cd26,10.1177/152397211201200103,Nonprofit Exemptions and Homeowner Property Tax Burden,2012.0,Public Finance and Management,Thad D. Calabrese; D. Carroll,"This paper examines the question of whether there is any correlation between the prevalence of nonprofit organizations with property, plant, and equipment exempt from property taxation and the property tax burden for homeowners. Data from the Tax Foundation and Internal Revenue Service was used to analyze general-purpose local governments within larger counties (populations greater than 65,000) in the United States for years 2005 and 2006. Several econometric specifications were used to estimate homeowner property tax burden as a function of the value of nonprofit fixed assets, government tax structure characteristics, and a series of control variables. Our estimates suggest that county geographies with greater presence of nonprofits tend to have higher homeowner tax burdens on average. Specifically, the value of nonprofit tax-exempt fixed assets within a county geography that is 10% above the mean of $15.4 million is generally associated with a median property tax paid by homeowners as a % of household income that is between 0.0009% and 0.0154% above the mean or between $2 and $24 higher on average. The median property tax paid as a % of homeowner's home value would be between 0.0",9.0,
cdb2caa275d1ae41b13d7933976746eda0a14b7f,10.1080/07352166.2020.1814154,Perceived reasons for paying more for housing in metro Atlanta: Results from the 2018 Metro Atlanta Speaks survey,2020.0,Journal of Urban Affairs,Ramesh Ghimire; Jim Skinner,"ABSTRACT Various factors may influence housing prices paid by residents, and these factors can be grouped into the demand- or supply-side factors. However, the empirical literature has not adequately investigated whether or not residents perceive that they pay more for housing, and if so, whether paying more is associated with the demand-side factors such as housing attributes or neighborhood/locational amenities, or supply-side factors such as a lack of affordable housing alternatives. Using the 2018 Metro Atlanta Speaks survey, we find that millennials, Blacks or African Americans, Hispanics or Latinos, residents with children, and relatively higher-income residents perceived that they are paying more because of the demand-side factors such as housing attributes or neighborhood/locational amenities. Conversely, working-age residents, females, high school graduates or less, and renters perceived that they are paying more due to the supply-side factors such as a lack of affordable housing alternatives. In a situation where residents are to choose one factor associated with housing, millennials and Blacks or African Americans prefer the quality of housing, relatively higher-income r",9.0,
d7c1bb267950582a66a82f6bfa4abfcb73afc1b8,10.1108/jerer-10-2020-0053,The determinants of real estate prices in a European context: a four-level analysis,2021.0,Journal of European Real Estate Research,A. M. Cunha; Júlio Lobão,"PurposeThis paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).Design/methodology/approachThe authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.FindingsThe results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.Practical implicationsForecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.Originality/valueTo the best of the authors knowledge, this i",9.0,
3b145fc34a20579d69b10544f4da1eace43b32a3,10.1177/23998083231180213,Three-dimensional land-use configuration and property prices: A spatially filtered multi-level modelling perspective,2023.0,Environment and Planning B Urban Analytics and City Science,Wei Zheng; Mingshu Wang,"The influence of neighbourhood characteristics on housing prices has gained increasing attention from scholars in recent decades. However, studies on the three-dimensional nature of urban space, and particularly the vertical dimension, have remained limited. This study investigates previously unexplored variables that can capture the vertical and horizontal dimensions of land-use configuration. In addition, this study proposes a spatially filtered multi-level approach to modelling variations in property values which can capture both spatial and multi-level effects. The research findings reveal a price premium for housing located in immediate neighbourhoods with more open mid-rise buildings and low plants. The results also demonstrate the varying effects of determinants of house pricing in spatially heterogeneous zones.",9.0,https://journals.sagepub.com/doi/pdf/10.1177/23998083231180213
3e57eaaf1dae734a6fb8779428f4ee93dccbfc25,10.1080/02673037.2022.2123902,The applicability of self-reported home values in housing wealth inequality assessment: evidence from an emerging country,2022.0,Housing Studies,Mateusz Tomal,"Abstract One of the measures of housing wealth inequality is the property’s market value. In existing analyses, this figure is often a subjective value determined by homeowners. Little is known about the validity of using this type of data as a substitute for market value in inequality studies. Therefore, this paper aims to examine whether self-reported home values can be applied to evaluate housing wealth inequality. In order to achieve this goal, first, a theoretical framework on the irrelevance of valuation bias for the assessment of housing wealth inequality was developed, followed by an empirical analysis. The latter included gathering information on subjective flat values and their characteristics in Warsaw. Next, a geographically weighted regression was calibrated to calculate the market value of these dwellings. Then, using the Gini coefficient, housing wealth inequality levels were estimated separately for subjective and objective home values. The results revealed that the former could serve as a very good proxy for the latter in housing wealth inequality analysis. The findings hold across almost all identified subgroups based on respondents’ gender, age, income, wealth, e",9.0,
ef01c25503e55a9abd199842a7d9a1013097bcc7,10.1080/24694452.2023.2187756,A Bayesian Implementation of the Multiscale Geographically Weighted Regression Model with INLA,2023.0,Annals of the American Association of Geographers,Zhi-Hua Ma; Zhe-Lin Huang,"The multiscale geographically weighted regression (MGWR) model is an important extension of the classical geographically weighted regression (GWR) model that can be used to explore the spatial nonstationarity of the regression relationship in spatial analysis, but also allows for different scales on conditional relationships between response and different predictors. A Bayesian version of the MGWR model is proposed to obtain estimates of the spatially varying coefficients and the bandwidths simultaneously. The hierarchical form of the Bayesian MGWR model has attractive features, including obtaining posterior estimates of the bandwidths and local parameters simultaneously, and their uncertainty can be easily measured. For Bayesian posterior inference, an efficient algorithm based on integrated nested Laplace approximation is introduced to provide a great alternative of the classical Markov chain Monte Carlo algorithm under the Bayesian framework. The performance of the proposed method is evaluated through simulation study, and it is shown that the proposed approach can correctly identify the differences between scales of parameter surfaces and also obtain precise posterior estimates",9.0,
ec674e9f69f5e6c26fae8058d895f759bd808c0b,10.1109/icced56140.2022.10010684,House Price Prediction Using Linier Regression,2022.0,International Conference Computing Engineering and Design,N. T. M. Sagala; Laura Hestia Cendriawan,"Property entrepreneurs will compete to build properties, especially houses for investment facilities. This will make house prices increase day by day with a high purchasing power of the people. Consumers will think in buying a house whether the house they buy will have a good profit value or not. The aim of the study is to build a model that may predict house price for company and become a business decision for consumers. The methodology used seven major steps namely, business understanding, data understanding, data cleaning, data standardization, modelling, and evaluation. This study develop a linear regression model for home price prediction and tests it using data from the Maribelajar company. These are carried out on the Azure Platform by creating two pipelines. One pipeline for training and another for testing. The results are then visualized using Power Business Intelligence for providing a proper business performance analysis. From the experimental results, the model achieved RMSE= 0.0334 and R coefficient = 0.7. The data analysis and testing in this study show that the multiple linear regression model can forecast and evaluate housing prices to some extent, but the algorith",9.0,
a3c09556797548cafbddff62b04c295357bb2adb,10.1016/j.jhe.2020.101712,Asylum seekers and house prices: Evidence from the United Kingdom,2020.0,Journal of Housing Economics,W. D. Lastrapes; Thomas Lebesmuehlbacher,"Abstract We estimate the effect of asylum seekers on house prices in England and Wales from 2004 to 2015, and find, using panel data and time series methods, that the government’s dispersal policies have had small but statistically significant negative effects on housing prices, especially for lower-priced and lower-quality housing units. Our panel data regressions rely on variation across time and localities, and account for endogeneity bias using instrumental variables, to identify these causal effects. Time series regressions depend on plausibly exogenous nationwide flows of new asylum seekers, and provide evidence that negative house price effects are larger in areas that supported Brexit.",9.0,https://doi.org/10.2139/ssrn.2868420
55cb74184884bd5ecbc4457d1d33a747eb6a7733,10.1007/s10436-012-0196-z,Identifying the determinants of mortgage default in Colombia between 1997 and 2004,2013.0,,J. Carranza; D. Estrada,This paper examines the relationship between mortgage default decisions and relevant observable variables in Colombia between 1997 and 2004. We estimate a discrete choice model of default using a panel of individual mortgage characteristics and payment information. We show that home prices and debt balances are the main determinants of mortgage default. We account for the presence of unobserved heterogeneity using Survey data and simulation techniques. Including controls for the distribution of income shows that income was not important but helps clarifying the relevance of mortgage leverage in the determination of the observed default rates.,9.0,http://link.springer.com/10.1007/s10436-012-0196-z
42bb8ba28b46f3d73881a3f086b175f35a8da86d,10.1111/jors.12439,A revealed preference index of urban amenities: Using travel demand as a proxy,2019.0,Journal of Regional Science,D. Broxterman; Chun Kuang,"We introduce an index of urban amenities based on hotel night stays that can rank most US metros for the years 1990 through 2015. This naive index can be used on its own to study changes in amenity level or with other measures to reduce omitted variable bias. Researchers can also use the index to create panel datasets from more sophisticated cross‐sectional indexes. Empirical testing shows the bundle of amenities associated with hotel stays has substantial effects on variation in housing prices across cities and time, validating our proxy variable approach. Testing for an association with wages is inconclusive.",9.0,https://doi.org/10.2139/ssrn.3196804
9379db67279cfdfdbc4114f587b85009d164054a,10.1080/02513625.2021.2026646,The Vanishing Suburban Dream in Australia,2021.0,disP - The Planning Review,Peter Walters,"Australia is the most suburbanised country on earth. The ‘Australian Dream’ of a house in the suburbs has long reflected the sense of entitlement that urban Australians have to the financial security, private space and amenity that the Dream promises. Since around the turn of the century, governments at every level have been attempting to arrest suburban sprawl for its negative social, environmental and economic impacts. However, neoliberal economic and planning environments have exacerbated regulatory failure, so attempts to create compact cities have done little to provide genuine alternatives to suburban life or address low-quality sprawl in newly built outer suburbs. Unrestrained house price inflation, assisted by an investor-friendly taxation regime, has led to a situation where the housing outcome, the Dream, enjoyed by previous generations is becoming impossible. Overlaying this is an ideological environment that pits the suburbs against the inner city as a battle in the ‘culture wars’ and serves to obliterate any nuance, diversity or possibility for progressive change into the public debate.",9.0,
8ce5117358b241b725e4e72d8f74f50fb36531de,10.1145/320435.320541,Natural language processing in intelligent information retrieval,1985.0,ACM Annual Conference,T. Doszkocs,"[role, u*e and users1 Operational online information retrieval systems such as BRS, DIALOG I ELHILL, LEXIS-NEXIS and ORBIT, to list just a few. arc the only practical, domain independent means available currently for the searching of large bodies of natural language text. These systems, along with the likes of COMPUSERVE. DOW JONES NEWS RETRIEVAL, NEWSNET and the SOURCE. appear to computer literate users as just another means of electronic information delivery, comparable to in-house infornation systems. online stock quotations, computerized real estate listings or databases on",9.0,https://dl.acm.org/doi/pdf/10.1145/320435.320541
572161e9059b07e805c820460ac1d693fd733798,10.1177/0042098018800576,Unbundling negative and positive externalities of nature in cities: The influence of wild animals on housing prices,2018.0,Social Science Research Network,Dani Broitman; Vladimir Griskin; D. Czamanski,"Proximity to nature is highly valued by urbanites, who demonstrate higher willingness to pay for housing at locations near open and green spaces. However, nature in cities can generate negative externalities as well. In this article, we illustrate the complex relationship between cities and nature and suggest that their balance is time and location specific. The article presents estimates of positive and negative externalities based on data about encounters of humans with wild animals in the city of Haifa, Israel, and residential property values nearby. The data were analysed to uncover spatial regularities and basic statistical relationships. The results reveal the presence of dominant positive externalities when the human–wild animals interaction is low, driven by proximity to open and green areas. However, in certain areas and under certain circumstances, the nuisance generated by higher probabilities of encounters with wild animals near dwelling areas is correlated with lower property prices, overcoming the positive externalities of location near natural areas.",9.0,
35de6ee00d6d525ab39128defbe8f7be523396ce,10.1016/j.physa.2019.04.096,A coevolution model of population distribution and road networks,2019.0,,F.-X. Zhao; F.-W. Zhao; H. Sun,"Abstract This paper proposes a coevolution model of population distribution and road networks. In the model, new roads are built with the formation of new communities, and these are connected to existing zones with higher population density with higher probability. The relative neighborhood graph and a Fermat–Weber location problem are introduced as the connection mechanism to capture the characteristics of population distribution and road networks. Moreover, two kinds of economic factors, accessibility and housing price, are considered to reflect the impacts on the coevolution between population distribution and road networks. Simulations and empirical experiments are conducted to demonstrate the interaction between population distribution and road networks. The topological attributes of the road network are characterized in the experiments using degree distribution, betweenness centrality, coverage, circuitness, and treeness. The experimental results show that the proposed model has reflect on the simulation.",9.0,
7efd41bc791b753e6271416805008f926271a937,10.3929/ethz-b-000332703,Locating Public Facilities: Theory and Micro Evidence from Paris,2019.0,Social Science Research Network,Gabriel Loumeau,"This paper proposes a novel approach to evaluate location decisions for public facilities. The approach addresses, not only the standard distance-minimizing problem, but also the endogenous location decisions of individuals. To do so, I develop a quantifiable general equilibrium model with endogenous (residential and commercial) densities, housing prices, commutes to work and public facilities, as well as public facility characteristics. The latter includes a facility’s location, quality, district and capacity. I apply the framework to secondary schools in Paris’ greater region at a 1km2 geographical scale. The analysis reveals that the observed location decisions made between 2001 and 2015 underestimate the endogenous reaction of individuals. A more decentralized strategy is predicted to increase welfare growth by 10 percentage points on average, mostly via shorter commutes and lower housing prices. JEL Codes: R530, H110, R120.",9.0,
6213dd2736adf021b6b4d4e17854e9b115c1d349,10.1109/kst51265.2021.9415771,Stacking Ensemble Learning for Housing Price Prediction: a Case Study in Thailand,2021.0,International Conference on Knowledge and Smart Technology,Gan Srirutchataboon; Saranpat Prasertthum; E. Chuangsuwanich; Ploy N. Pratanwanich; Chotirat Ratanamahatana,"In this paper, we analyze the housing price data obtained from a leading Thai real estate website and Open Street Maps (OSM) to identify the features that affect the housing price in Thailand from 2015 to 2019. Moreover, we propose a model based on a stacking ensemble learning framework, where the predictions are generated by stacking three base learning models consisting of a convolutional neural network (CNN), an ensemble model (such as random forests (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost)) and a simple linear regression technique. The CNN is used to extract features from house images which are then combined with traditional features to estimate the initial price. The prediction is then calibrated using linear regression. Compared to individual models, the proposed model achieves a Mean Absolute Percentage Error (MAPE) of 17.83%, significantly outperforming other baselines.",9.0,
e9b23209ba31dae8d87bf9d7577d6230db2e2799,10.1109/icecaa55415.2022.9936336,Machine Learning based House Price Prediction Model,2022.0,2022 International Conference on Edge Computing and Applications (ICECAA),Chee-Kin Chen; Zailan Arabee Bin Abdul Salam; Kadhar Batcha Nowshath,"In this digital era, People have become more aware on purchasing a new property. Many digital tools have been developed to analyze the property marketing strategies and the buyers' budget constraints. The goal of this paper is to predict house prices for non-home owners based on their financial resources and aspirations. Estimated prices will be calculated by using different tools such as Machine Learning (ML), Artificial Neural Network (ANN) and Chatbot. All of the above-mentioned techniques were used here to determine the most effective house price from the collected dataset. This research project will particularly conduct multiple researches on the affordability of houses present within Malaysia. The motive of this work is to build a prediction model to help in the process of house price prediction and assist both buyers and seller to have a general view on the current market price and trend.",9.0,
c7e8610965fd640404f90f1399ee4543707fd819,10.1016/j.indmarman.2008.01.001,New printing technology and pricing,2009.0,Industrial Marketing Management,P. Hultén; Magnus Viström; Thomas Mejtoft,"This case study analyzes five Swedish printing houses' pricing with respect to their investments in new printing technology. The new printing technology made it possible for the printing houses to market new products and services to meet the demand for shorter delivery times and full service solutions. Although this demand was apparent, the printing houses' opportunities to capitalize on their investments depended on the characteristics of the market segment that they served. Findings indicate that the new printing technology made it possible to change prices when the new services reduced delivery time and costs, and when there were substantial differences between the new services and available substitutes. Thus, customers accepted new pricing when the utilization of the new technology resulted in financial gains and time reductions.",9.0,
8592916963758e79b520fdc79b6c265df91ce65a,10.1109/icace.2018.8687080,Survey on predicting property price,2018.0,International Conference Automation and Computational Engineering,N. Shinde; Kiran Gawande,"Prediction involves extracting the useful, understandable information from the raw data to forecast the unknown, whether the unknown is past, present or future. Thus, predictive analytics is a branch of data mining used to predict or forecast a trend or a behavior. Prediction is used in various fields like credit scoring, weather forecasting, healthcare, travel, financial sector and many more. This paper strives to explain the application of Predictive Analytics in the field of Real Estate. The paper aims at putting forward, the various predictive techniques used to forecast the property prices. The paper mentions different research work done in the real estate domain. The paper also describes in depth about the description of Neural Network and Decision Tree used to predict the sale price of the house.",9.0,
93479e6b21fce5b14ca7c0f3bafd2b5aac5b23fd,10.1016/j.jkss.2017.12.002,Quantile regression for robust inference on varying coefficient partially nonlinear models,2018.0,Journal of the Korean Statistical Society,Jing Yang; Fang Lu; Hu Yang,"Abstract In this paper, we propose a robust statistical inference approach for the varying coefficient partially nonlinear models based on quantile regression. A three-stage estimation procedure is developed to estimate the parameter and coefficient functions involved in the model. Under some mild regularity conditions, the asymptotic properties of the resulted estimators are established. Some simulation studies are conducted to evaluate the finite performance as well as the robustness of our proposed quantile regression method versus the well known profile least squares estimation procedure. Moreover, the Boston housing price data is given to further illustrate the application of the new method.",9.0,
90048d6d48c69e36fe82b7c2f62d837956e9144d,10.1109/idap.2018.8620781,Predicting Housing Price Based on Ensemble Learning Algorithm,2018.0,2018 International Conference on Artificial Intelligence and Data Processing (IDAP),Ya-Juan Tang; Shuang Qiu; Pengcheng Gui,"The housing prices are affected by many factors. Finding a practical prediction algorithm is becoming an important issue. Because the effect of a single classifier is limited. In this paper, we select a real estate transaction instance of a city in the United States, and employ the bagging algorithm and random forest to construct a model to predict housing price. Ensemble learning which combines multiple base learners, which can obtain excellent generalization performance and improves the prediction accuracy. The results show that ensemble learning has better prediction accuracy than the decision tree model.",9.0,
8a6d8afe7294a3451b69dccd159bf588326dfa82,10.1016/j.regsciurbeco.2024.103984,Fire protection services and house prices: A regression discontinuity investigation,2024.0,Regional Science and Urban Economics,David M. Brasington; O. Parent,,9.0,https://doi.org/10.1016/j.regsciurbeco.2024.103984
d7b4864ef0252581d29f8d728e2be6f753505eea,10.1007/s43546-022-00358-4,Predicting residential property value: a comparison of multiple regression techniques,2022.0,SN Business & Economics,Lee Whieldon; Huthaifa I. Ashqar,,9.0,https://doi.org/10.13016/m2d15w-hqpn
5024302296ca0c8870c5d64cebe6f2e1619ceb86,10.3386/w17200,Understanding the Solar Home price premium: Electricity generation and ''Green'' social status $,2011.0,,Samuel R. Dastrup; Josh Graff Zivin; Dora L. Costa; Matthew E. Kahn,,9.0,https://doi.org/10.3386/w17200
988d4549282509e934b142f99eac4c2da0dcdf34,10.1080/14445921.2007.11104222,Combining Geographic Information Systems and Regression Models to Generate Locational Value Residual Surfaces in the Assessment of Residential Property Values,2007.0,,Amalina Izzati Abdul Hamid,,9.0,http://www.prres.net/Papers/PRPRJ_No_1_2007_Hamid.pdf
fed7f3cb0648a7182a0bb7a5ddd8bb95551f8a7d,10.1109/rpc.2018.8482191,Predicting Sales Prices of the Houses Using Regression Methods of Machine Learning,2018.0,2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC),Parasich Andrey Viktorovich; Parasich Viktor Aleksandrovich; Kaftannikov Igor Leopoldovich; Parasich Irina Vasilevna,"In this article we will describe our solution for “House Prices: Advanced Regression Techniques” machine learning competition, which was held on Kaggle platform. The competitor's goal was to predict house's sale price by their attributes like house area, year of building, etc. In our solution, we use classic machine learning algorithms, and our original methods, which will be described here. At the end of the competition, we took 18th place among 2124 participants from whole world (top 1%).",9.0,
969f6f24a3aadf9859ebe39ba828784323a35565,10.1007/s10901-017-9572-3,Effects of the housing price to income ratio on tenure choice in Taiwan: forecasting performance of the hierarchical generalized linear model and traditional binary logistic regression model,2018.0,,Chun-Chang Lee; Chih-Min Liang; Jian-Zheng Chen; C. Tung,,9.0,
f28b96fe92f004ac16c87bc0c2d3ada98f360756,10.1080/02673030701254152,The Determinants of Home Price Appreciation Among Community Reinvestment Homeowners,2007.0,,Michael A. Stegman; Roberto G. Quercia; W. R. Davis,,9.0,
6cf228a74f47bb545361763dd3e210f0073754fe,10.1108/ijhma-09-2023-0125,"Relationship between housing, oil, gold and stock markets: evidence from UK and Norway",2023.0,International Journal of Housing Markets and Analysis,Z. Göknur Büyükkara; İsmail Cem Özgüler; Ali Hepşen,"
Purpose
The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.


Design/methodology/approach
This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.


Findings
The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR ",8.0,
b12aa5220d76687247e4b8ac3ea520d56bedc8c4,10.1016/j.ssmph.2016.09.001,Neighborhood Disadvantage and Preterm Delivery in Urban African Americans: The Moderating Role of Religious Coping,2016.0,SSM: Population Health,S. Sealy-Jefferson; Jaime C. Slaughter-Acey; C. Caldwell; Jamila L. Kwarteng; D. Misra,"Evidence suggests that neighborhood disadvantage predicts preterm delivery (PTD). However, the design of most existing studies precludes within-group analyses, which would allow the identification segments of the population at highest risk, as well as preventive factors. African Americans (AA) are disproportionately affected by PTD, are disproportionately concentrated in disadvantaged neighborhoods, and frequently use religious coping in response to chronic stressors. Our objective was to examine the association between neighborhood disadvantage and PTD, and whether religious coping moderated the associations, among postpartum AA women. Addresses from participants of the Life Influences on Fetal Environments Study (n=1387) were geocoded and linked to data from the American Community Survey. An index of neighborhood disadvantage was derived from a principal components analysis of the following variables: % below poverty, % unemployed, % receiving public assistance income, % college educated, % AA, % female-headed households, % owner occupied homes, median income, and median home value. Three domains of religious coping were assessed: organizational (church attendance), non-organizat",8.0,https://www.sciencedirect.com/science/article/pii/S2352827316300659/pdf
42ade4696d73aa54dfaac36f19ef8d1f767181d6,10.29313/jrs.vi.905,Pemodelan Faktor-Faktor yang Mempengaruhi Harga Rumah di Jabodetabek Menggunakan Metode Regresi Probit,2022.0,Jurnal Riset Statistika,Fifia Febyanti,"Abstract. In addition to clothes and food, houses are one of the basic needs. The lifestyle of dynamic modern society tends to require houses with various public and social facilities in one area. Property value will be formed through a factor of the completeness of the characteristics of the house and vehicle or facilities available. The price is a measuring device in the form of money, where the price can be obtained by a number of goods or services through the exchange process and obtain the rights and benefits of these goods or services. In this study want to know what factors affect house prices in Jabodetabek by using ordinal probit regression, where home prices consist of three categories, namely cheap, medium and expensive. The data used secondary data obtained from the site of the roof. MAI, namely the dataset of house prices in Indonesia. Based on the data analysis can be seen that the factors that influence the price of houses in Jabodetabek are the type of house, certificate, number of bedrooms, the number of bathrooms, and floors. The accuracy of the classification of 25.6% percent and the value of pseudo  mcfadden of 57.57 percent. 
Abstrak. Selain pakaian dan makanan",8.0,https://journals.unisba.ac.id/index.php/JRS/article/download/905/675
ed6fcbec3e62a6b10ac283a1ff0d7bab2ce2d34a,10.23895/kdijep.2010.32.4.1,Risk Analysis of Household Debt in Korea: Using Micro CB Data,2010.0,,Joon-Ho Hahm; Jung-in Kim; Young Sook Lee,"We conduct a comprehensive risk analysis of household debt in Korea for the first time using the whole sample credit bureau (CB) data of 2.2 million individual debtors. After analysing debt service capacity profiles of debtor groups classified by the borrower characteristics such as income, age, occupation, credit scoring, and the type of creditor business companies, we investigate the impact of interest rate and income changes on debt service-to-income ratios (DTIs) and default rates of respective debtor groups. Empirical results indicate that debt service burdens are relatively high for low income wage earners, high income self-employed, low income capital and card loan holders, and high income mutual savings loan holders. We also find that debtors from multiple financial companies are particularly weak in their debt service capacity. The scenario analysis indicates that financial companies, with the current level of capital buffers, may be able to absorb negative consequences arising from the increase in DTIs and loan default rates if the interest rate and income changes remain modest. However, the negative consequences may fall disproportionately on non-bank financial companies",8.0,https://doi.org/10.23895/kdijep.2010.32.4.1
9a91393df93c7ca2ebd21ed38a6f4fe088cf3bd9,10.3390/su15097026,"Spatial–Temporal Variation and Influencing Factors on Housing Prices of Resource-Based City: A Case Study of Xuzhou, China",2023.0,Sustainability,Qing Yao; Ying Hu,"The steady development of the real estate market is an important link in the transformation of resource-based cities. Taking the main urban area of Xuzhou, a typical resource-based city in China, as an example, this study used a spatial–temporal geographically weighted regression (GTWR) method guided by characteristic price theory to analyze the evolution of the spatial–temporal pattern of house prices in resource-based cities and the influencing factors. The results of the study showed that, from the time trend, Xuzhou’s main city house price trend underwent the obvious stages; from June 2015 to September 2016, the trend was stable, and from March 2017 to June 2018, it was an “up–down–up” trend. With respect to the spatial distribution of house prices, from June 2015 to June 2017, the traditional business district and the major city in the Xuzhou metropolitan area were the areas of highest house prices, and after September 2017, the structural features of the traditional business district, along with the main city of the metropolitan area and the new city, formed the areas of highest importance. With regard to the factors influencing house prices, the type of dwelling, according t",8.0,https://www.mdpi.com/2071-1050/15/9/7026/pdf?version=1682149803
89b053bb4016a5117c8a06e5f807933acb26e20b,10.3389/fenvs.2024.1466542,"Identification of surface thermal environment differentiation and driving factors in urban functional zones based on multisource data: a case study of Lanzhou, China",2024.0,Frontiers in Environmental Science,Yi-Xuan Wang; Shu-Wen Yang,"The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of “population heat—land use scale” to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban",8.0,https://doi.org/10.3389/fenvs.2024.1466542
417361af9668fb5322acbac92a07b2e6c18d4957,10.3390/land11010012,Forest Transition and Metropolitan Transformations in Developed Countries: Interpreting Apparent and Latent Dynamics with Local Regression Models,2021.0,Land,Leonardo Bianchini; R. Salvia; G. Quaranta; G. Egidi; L. Salvati; A. Marucci,"Metropolitan fringes in Southern Europe preserve, under different territorial contexts, natural habitats, relict woodlands, and mixed agro-forest systems acting as a sink of biodiversity and ecosystem services in ecologically vulnerable landscapes. Clarifying territorial and socioeconomic processes that underlie land-use change in metropolitan regions is relevant for forest conservation policies. At the same time, long-term dynamics of fringe forests in the northern Mediterranean basin have been demonstrated to be rather mixed, with deforestation up to the 1950s and a subsequent recovery more evident in recent decades. The present study makes use of Forest Transition Theory (FTT) to examine spatial processes of forest loss and expansion in metropolitan Rome, Central Italy, through local regressions elaborating two diachronic land-use maps that span more than 80 years (1936–2018) representative of different socioeconomic and ecological conditions. Our study evaluates the turnaround from net forest area loss to net forest area gain, considering together the predictions of the FTT and those of the City Life Cycle (CLC) theory that provides a classical description of the functioning of",8.0,https://www.mdpi.com/2073-445X/11/1/12/pdf?version=1640156169
a8948ef44ecc20b130b61bbea630a8b924994280,10.1371/journal.pone.0256710,"Study on the change characteristics and market driving factors of residential land price in the Beijing Tianjin Hebei urban agglomeration, China",2021.0,PLoS ONE,Pengfei An; Can Li; Yajing Duan; J. Ge; Xiao-Miao Feng,"Land prices are the key problem of urban land management, with prices of residential land being the most sensitive and the strongest social reflection among the different land types. Exploring spatial and temporal variation of residential land prices and the effect of land market factors on residential land prices can help the government formulate targeted regulations and policies. This study analyzes the spatial and temporal evolution of residential land prices and the factors influencing the land market in the Beijing-Tianjin-Hebei region based on land transaction data from 2014–2017 using exploratory spatial data analysis (ESDA) and a geographically weighted regression (GWR) model. The results show the following: ① Residential land prices in Beijing and Tianjin are significantly higher than those in other regions, while Zhangjiakou, Chengde, and western mountainous areas have the lowest residential land prices. Over time, a development trend of residential land price polycentricity gradually emerged, and the locational correlation has gradually increased. ② Under the influence of the land finance model of local governments in China, three factors, namely, the land stock utilizat",8.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0256710&type=printable
58258562c2ba65eb7233804711edfd996590d33a,10.23919/jsc.2021.0003,Estimating Multiple Socioeconomic Attributes via Home Location - A Case Study in China,2021.0,Journal of Social Computing,Shichang Ding; Xin Gao; Yufan Dong; Yiwei Tong; Xiaoming Fu,": Inferring people’s Socioeconomic Attributes (SEAs), including income, occupation, and education level, is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation. Previous works mainly focus on estimating SEAs from peoples’ cyberspace behaviors and relationships, such as the content of tweets or the social networks between online users. Besides cyberspace data, alternative data sources about users’ physical behavior, like their home location, may offer new insights. More specifically, in this paper, we study how to predict a person’s income level, family income level, occupation type, and education level from his/her home location. As a case study, we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China. We further enrich home location with the knowledge from real estate websites, government statistics websites, online map services, etc. To learn a shared representation from input features as well as attribute-specific representations for different SEAs, we propose H2SEA, a factorization machine-based multi-task learning method with a",8.0,https://ieeexplore.ieee.org/ielx7/8964404/9355030/09355035.pdf
6d05d71243760482acbe9d21b5e43699b5d849c2,10.1016/b978-0-12-815859-3.00021-4,Application of Panel Data Models for Empirical Economic Analysis,2019.0,Panel Data Econometrics,K. Bhattarai,"Abstract This chapter reviews important applications of panel data models. The process of substitution of labor by capital as discussed in Karabarbounis and Neiman (2014) and Picketty (2014) have increased the capital share causing reduction in labor share of about 10 percent magnitude, impacts of trade and aid on economic growth. Fixed and random effect estimates show that investment rather than aid was a factor contributing to growth. Exports tied to aid are always harmful for growth of recipient countries. Although the evidence is mixed for the individual economies, there certainly appears trade-offs between unemployment and inflation in the panel of OECD countries as shown by the random- and fixed-effect models in which the Hausman test is in favor of random-effect model. A simple VAR model with two lags on inflation (πi,t) and unemployment (ui,t) shows persistence of inflation and unemployment rates among the OECD economies. The ratio of investment to GDP is a significant determinant of growth rates across OECD countries, and FDI contributes positively to growth. Higher tax rates cause lower growth rates, which is intuitive. Overall fit of this model is good because R2 is 42 p",8.0,https://hull-repository.worktribe.com/output/3352509
6085cbc4c4e0a732b17921bfa283a72758b9db0c,10.1108/jerer-02-2022-0005,"House price dynamics in Iberian Metropolitan Statistical Areas: slope heterogeneity, cross-sectional dependence and elasticities",2022.0,Journal of European Real Estate Research,A. M. Cunha; Júlio Lobão,"PurposeThis paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.Design/methodology/approachThe authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.FindingsThe results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is",8.0,
d6f1081c301b96a5b5d86b3f10ce29dd20a47e27,10.3390/su15076172,Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation,2023.0,Sustainability,Sui Zeng; Jiahao Zhang; Jian-Ru Tian,"High-density urban areas have spatial characteristics, such as complex functions, population gathering, and complex forms, that lead to more severe urban heat island effects. Systematically evaluating the thermal environmental benefits of urban spatial forms to optimize the urban physical environment is important. In this study, Tianjin’s central urban area, which is a typical representative of high-density urban areas, was selected to invert the multi-period land surface temperature by relying on the existing two- and three-dimensional morphological data set of communities. The multi-scale geographically weighted regression model was used to fit the regression relationship between the urban land surface temperature and spatial morphological parameters. From this, the influencing factors of different types of existing community spaces and their spatial stabilities were explored. The results show the following: (1) The summer surface temperature varies greatly in the central urban area, and the high-temperature areas are mainly distributed in the industrial, residential, and commercial districts. (2) The MGWR model has the better model-fitting ability. The positive influence coeffic",8.0,https://www.mdpi.com/2071-1050/15/7/6172/pdf?version=1680532361
cd6e8837aa1517e46759a245ae1b07679ef525a9,10.3389/fenvs.2023.1343340,"Examining the impacts of urban, work and social environments on residents’ subjective wellbeing: a cross-regional analysis in China",2024.0,Frontiers in Environmental Science,Zhou Fang; Yuxin Liao; Chang Ma; Rong Wu,"Introduction: Urbanization has affected the quality of the living environments. It is important to improve residents’ living environments and promote their happiness.Methods: We use a national and representative dataset targeting the labor force in China, collecting basic information about the work environments, the social environments, and the urban environments. This work employed a linear regression model to investigate the influencing factors of residents’ SWB.Results: The three major findings are as follows: 1) At the national level, residents’ SWB exhibits a notable spatial variability, being higher in the northern regions and lower in the southern areas. 2) The dimensions of the urban environments (housing prices, POI density, NPP, land development intensity and the number of tertiary hospitals), the work environments (work pressure and job satisfaction), and the social environments (community trust, social justice and sense of security), along with sociodemographic characteristics, significantly influence SWB. 3) In China, the impacts on SWB exhibit pronounced regional heterogeneity. The relationship between environmental pollution and SWB is characterized by an inverted U-",8.0,https://www.frontiersin.org/articles/10.3389/fenvs.2023.1343340/pdf?isPublishedV2=False
0552e93c44c913c13038b493225c06140ce8e1f8,10.2139/ssrn.3357359,Coherent Estimations for Residential Photovoltaic Uptake in Germany Including Spatial Spillover Effects,2019.0,Social Science Research Network,J. Baginski; C. Weber,"The share of solar energy in German electricity generation has increased strongly over recent years. This is largely due to guaranteed feed-in tariffs together with decreasing prices for solar panels. Residential PV systems play a decisive part providing households with a possibility to contribute to the Energiewende and benefit from the use of renewable energy. Their regional distribution varies distinctly across Germany implying different requirements in distribution grids as well as uneven utilization of national policy measures. Our paper focusses on the spatial diffusion of roof mounted PV systems and the underlying drivers in Germany. We extend previous findings not only by including additional explanatory variables but also by considering cross-regional spillover using spatial econometric models. Estimation results show that spatial dependence is a relevant determinant for explaining regional clusters of PV adoption. Recurrent visual perception or peer-effects might explain spatial autocorrelation as potential adopters follow decisions by actors in the proximity. Another reason for spatial dependence might be a concentration of craft skills or solar initiatives, which leads ",8.0,https://www.econstor.eu/bitstream/10419/201585/1/wp1902.pdf
90428ff564e2df38bb9bc527181d4713daa5df98,10.1111/j.1936-704x.2018.03295.x,"Water Trading: Innovations, Modeling Prices, Data Concerns",2018.0,Journal of Contemporary Water Research & Education,B. Colby; Rowan Isaaks,": This article examines policy innovations and data concerns related to water trading in Colorado, and develops econometric models of transactions occurring over two distinct time periods. The Punctuated Equilibrium Theory (PET) of policy adaptation is used to examine shifts in Colorado water trading policy paradigms. Creating better policy frameworks for water trading is a key concern for agricultural, urban, and environmental water interests, given hotter temperatures and more variable precipitation patterns in the western U.S. Contractual arrangements of varying types are being used to engage farmers in providing reliable water supplies for ecosystem and urban needs through changes in farm water use practices. While various pieces of information about changes in water use can be gleaned from public databases, transaction price information is notably lacking. Recent Colorado policy innovations related to water trading emphasize reducing on-farm consumptive use and making water available for other purposes without permanently drying up irrigated cropland. The use of econometric models analyzing water rights transactions provides insight into how changes in key external factors aff",8.0,https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=1897&context=jcwre
b48a2331a5209d9875aae93fea40603ae8e99733,10.1088/2632-072x/ac9772,Local multifractality in urban systems—the case study of housing prices in the greater Paris region,2022.0,Journal of Physics: Complexity,Janka Lengyel; Stéphane G. Roux; François Sémécurbe; S. Jaffard; P. Abry,"Even though the study of fractal and multifractal properties has now become an established approach for statistical urban data analysis, the accurate multifractal characterisation of smaller, district-scale spatial units is still a somewhat challenging task. The latter issue is key for understanding complex spatial correlations within urban regions while the methodological challenge can be mainly attributed to inhomogeneous data availability over their territories. We demonstrate how the approach proposed here for the multifractal analysis of irregular marked point processes is able to estimate local self-similarity and intermittency exponents in a satisfactory manner via combining methods from classical multifractal and geographical analysis. With the aim of emphasizing general applicability, we first introduce the procedure on synthetic data using a multifractal random field as mark superposed on two distinct spatial distributions. We go on to illustrate the methodology on the example of home prices in the greater Paris region, France. In the context of complex urban systems, our findings proclaim the need for separately tackling processes on the geolocation (support) and any att",8.0,https://iopscience.iop.org/article/10.1088/2632-072X/ac9772/pdf
668bb5c67154844d8d06747af6775afb01c2a39a,10.1109/access.2024.3368067,ERDeR: The Combination of Statistical Shrinkage Methods and Ensemble Approaches to Improve the Performance of Deep Regression,2024.0,IEEE Access,Zari Farhadi; M. Feizi-Derakhshi; Hossein Bevrani; Wonjoon Kim; Muhammad Fazal Ijaz,"Ensembling is a powerful technique to obtain the most accurate results. In some cases, the large number of learners in ensemble learning mostly increases both computational load during the test phase and error rate. To solve this problem, in this paper we propose an Ensemble of Reduced Deep Regression (ERDeR) model, which is a combination of Deep Regressions (DRs), shrinkage methods, and ensemble approaches. The framework of the proposed model contains three phases. The first phase includes base regressions in which parallel DRs are used as learners. The role of these DRs is to extract features of input data and make prediction. In the second phase, to automatically reduce and select the most suitable DRs, shrinkage methods such as Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (EN) are employed. These models are compared with the non-shrinkage model. The last phase is ensemble phase, which consists of three different ensemble methods namely Multi-Layer Perceptron (MLP), Weighted Average (WA), and Simple Average (SA). These ensemble methods are used to aggregate the remaining learners from previous steps. Finally, the proposed model is applied to Monte Carl",8.0,https://ieeexplore.ieee.org/ielx7/6287639/6514899/10441809.pdf
44b5b4e67dc7ad9fe4ff2a8de72800ff8f9208ab,10.3390/land14020356,What Are the Pivotal Factors Influencing Housing Prices? A Spatiotemporal Dynamic Analysis Across Market Cycles from Upturn to Downturn in Wuhan,2025.0,Land,Tian-Chen Liu; Jingjing Wang; Ling-Bo Liu; Zheng-Hong Peng; Hao Wu,"With the new phase of urbanization in China, enhancing urban spatial quality has become a key task in urban development. As an important indicator of residents’ willingness to live, housing prices provide valuable feedback from their perspective for improving spatial quality. Taking Wuhan as a case study, this paper constructs an indicator system with 12 explanatory variables, including a subjective evaluation of buildings generated using deep learning techniques. Using OLS and GWR models, the study analyzes the factors influencing housing prices and their spatiotemporal dynamics in Wuhan’s core urban areas from 2016 to 2024, encompassing the full cycle of housing price fluctuations from an upward to a downward trend. The findings reveal that, as housing prices return to more rational levels, the impact of location factors diminishes, while the influence of community quality factors—such as property fees, green space ratio, and building quality—significantly increases. Factors such as proximity to hospitals also exhibit a certain degree of spatiotemporal complexity. This trend highlights residents’ growing attention to housing quality and living environments, marking a fundamental ",8.0,https://doi.org/10.3390/land14020356
63d0fca33adae59cd1330ba9da2372dd3f5cfe09,10.1109/access.2022.3210281,Graph Convolutional Network-Based Model for Megacity Real Estate Valuation,2022.0,IEEE Access,Zong-Mou Yang; Z. Hong; Ru-Yan Zhou; Hong Ai,"It is challenging to make precise assessments of real estate prices due to its elevated individual prices, complicated influencing factors, and ambiguous attribute selection. As a result of the high demand for owner-occupied and investment properties, real estate is also a substantial concern for society. A hot topic for research by major institutions has been how to accurately estimate its price. Real-world applications of real estate valuation impose stringent requirements on the acquisition of datasets and the generalizability of models. On the basis of SRGCNN, a spatial regression model with excellent generalizability, this paper introduces an external attention mechanism to construct the A-SRGCNN model and compares it to the benchmark model utilizing data from Shanghai, Melbourne, and San Diego. For spatial regression, A-SRGCNN employs graph convolutional neural networks, and the external attention mechanism implicitly considers the relationship between property data. Experiments indicate that the A-SRGCNN model outperforms the benchmark model and has improved real estate price estimation accuracy. In the meantime, this paper employs the A-SRGCNN model to conduct zonal experim",8.0,https://ieeexplore.ieee.org/ielx7/6287639/6514899/09904611.pdf
f2cc6a297bdb1aab7d471ec639e4bab628cd42ef,10.11591/ijeecs.v17.i3.pp1467-1473,Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach,2020.0,,S. Diwan,"Real estate forecasting has become an integral part of the larger process of business planning and strategic management in real estate sector. This study covers residential estate markets and concentrates on property types, while previous studies that have considered country wide house price indices. There is a gap identified in the literature which need to study correlations between property types within a region or a city and whether they will provide diversification benefits for real estate investors such as risk reduction per unit of returns. This aim of the paper is to propose and develop a computer assisted real estate property price forecasting model. This proposed framework will examine the current uses of artificial intelligence, particularly combining case base reasoning and artificial neural network, in the business-forecasting field and considers suitable applications in real estate. The methodology consists of five phases: 1) Data gathering b) Data cleaning c) ANN Training d) CBR (Case Base Reasoning) similarity retrieval e) Result retrieval. This research will investigate the influence of residential real estate property characteristics on property values (prices) in ",8.0,http://ijeecs.iaescore.com/index.php/IJEECS/article/download/18752/13532
8b218ce3bfc0497ed7581ad7c2bd3e5e0173498f,10.1093/sf/soac059,Paws on the Street: Neighborhood-Level Concentration of Households with Dogs and Urban Crime,2022.0,Social forces; a scientific medium of social study and interpretation,Nicolo P. Pinchak; Christopher R. Browning; Bethany Boettner; Catherine A. Calder; Jake Tarrence,"Abstract:The formative work of Jane Jacobs underscores the combination of ""eyes on the street"" and trust between residents in deterring crime. Nevertheless, little research has assessed the effects of residential street monitoring on crime due partly to a lack of data measuring this process. We argue that neighborhood-level rates of households with dogs captures part of the residential street monitoring process core to Jacobs' hypotheses and test whether this measure is inversely associated with property and violent crime rates. Data from a large-scale marketing survey of Columbus, OH, USA residents (2013; n = 43,078) are used to measure census block group-level (n = 595) rates of households with dogs. Data from the Adolescent Health and Development in Context study are used to measure neighborhood-level rates of trust. Consistent with Jacobs' hypotheses, results indicate that neighborhood concentration of households with dogs is inversely associated with robbery, homicide, and, to a less consistent degree, aggravated assault rates within neighborhoods high in trust. In contrast, results for property crime suggest that the inverse association of dog concentration is independent of ",8.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10106924
25598b542f05688c28f2c38b4f2bace88b9778ce,10.3390/ijerph192416382,"Do Vibrant Places Promote Active Living? Analyzing Local Vibrancy, Running Activity, and Real Estate Prices in Beijing",2022.0,International Journal of Environmental Research and Public Health,Yuan Lai; Jiatong Li; Jia-Chen Zhang; Lan Yan; Yifeng Liu,"Although extensive research has investigated urban vibrancy as a critical indicator for spatial planning, urban design, and economic development, the unclear relationship between local vibrancy and active living needs to be clarified and requires more in-depth analysis. This study localizes urban vibrancy at both hyper-local and neighborhood scales by integrating high-resolution, large-scale, and heterogeneous urban datasets and analyzing interactions among variables representing vibrancy’s environmental, economic, and social aspects. We utilize publicly available urban open data, Points of Interest requested from API, and leisure running trajectories acquired through data mining to investigate the spatial distribution of various vibrancy indicators and how they interact with physical activity at the local scale. Based on these variables, we then construct linear regression models and Geographically Weighted Regression (GWR) models to test and estimate how local vibrancy and physical activity relate to residential real estate characteristics. The results reveal the strong impact of urban form on local vibrancy but not physical activeness. At the neighborhood level, all vibrancy fac",8.0,https://www.mdpi.com/1660-4601/19/24/16382/pdf?version=1670398079
95c08692ba9aa15e109a365c3c0a6adee60bd5f5,10.3390/su15021690,Heterogeneous Impacts of Policy Sentiment with Different Themes on Real Estate Market: Evidence from China,2023.0,Sustainability,Diandian Ma; Ben-Fu Lv; Xue-Rong Li; Xiu-Ting Li; Shuqin Liu,"This paper empirically investigates the heterogeneous impacts of the media sentiment about policies with different themes on the real estate market in China. Based on the policy texts collected from both official and unofficial sources, we construct sentiment indices to capture the sentiment about policies with different themes, including real estate policies, fiscal policies, monetary policies, land policies, healthcare policies, household registration policies, and education policies, using text mining methods. Mediation models and GARCH models are then established to examine the impact of these sentiment indices on the real estate market. The E-GARCH model is established to examine the asymmetric effect of positive and negative sentiment on real estate market. The results show the following: (1) The real estate market in China is more affected by the policy sentiment on official media compared with the unofficial ones. (2) Policy sentiment affects the real estate price through the mediating variables of interest rate, real estate construction area, and real estate sales. (3) The impacts of sentiment with different themes on the volatility of the real estate market are heterogene",8.0,https://www.mdpi.com/2071-1050/15/2/1690/pdf?version=1673853373
fe9b5a96e7007d52e9bebe9eaea418541aa3b090,10.3390/su14010309,An Automatic Tool for the Determination of Housing Rental Prices: An Analysis of the Italian Context,2021.0,Sustainability,F. Tajani; F. Di Liddo; Rossana Ranieri; D. Anelli,"In the last decades, some global events such as the economic crisis of 2008 and the COVID-19 emergency of 2020, have generated more attention towards the housing rental market and its capacity to meet several social needs. In order to study the existent demand for houses, then define the interventions on the residential assets to make them more affordable for the most fragile population groups, adequate evaluation tools are required. With reference to the residential property segment of five metropolitan cities located in the Italian territory, the present research is aimed at analyzing the contribution of the most influencing factors on rental prices. In particular, this research refers to the rented properties of the second half of 2019, with a set of variables that represent the intrinsic and extrinsic factors of the local market. The implementation of an automated valuation model allows the determination of the most significant factors and the functional relationships that they have with housing rental fees. The outputs obtained could support the improvement of equitable public housing policies or could guide private investment decisions, such as refurbishment interventions of ",8.0,https://www.mdpi.com/2071-1050/14/1/309/pdf?version=1640767011
58af27d970ab7e2f303f03f2b00a1831b878bfa5,10.1108/ijhma-06-2021-0068,A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies,2021.0,International Journal of Housing Markets and Analysis,S. Raza; Nida Shah; Muhammad Tahir Suleman; Md. Al Mamun,"
Purpose
This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period.


Design/methodology/approach
This study uses the MF-DFA to analyze house price fluctuations.


Findings
The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result.


Originality/value
The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices ",8.0,
45bf23ac82ad0e1b5f36096ced78d28da398d9bb,10.1038/s41598-024-55129-y,Subjective social integration and its spatially varying determinants of rural-to-urban migrants among Chinese cities,2024.0,Scientific Reports,Qi-Long Chen; Chengxiang Wang; Pinrong He; Anning Cai,"Social integration, a huge issue triggered by migration, leads to potential social fragmentation and confrontation. Focusing on the precise enhancement of ""inner"" subjective social integration is the ultimate urbanization solution to enhance people-centered well-being and promote full social integration. This article used data from the China Migrants Dynamic Survey 2017 (CMDS 2017) to reveal the spatial patterns and mechanisms of subjective social integration in Chinese cities. We make an innovative attempt to introduce multiscale geographically weighted regression (MGWR) to address the appropriateness of policy formulation by addressing the spatial variation in the factors. The results demonstrate that the influences on subjective social integration have a strong spatial heterogeneity in China, a vast and unevenly developed country. Expanding on the typical factors, household registration and political participation affect North China more than other regions; and housing and marriage have a greater impact in South China, especially in the Pearl River Delta and the Eastern Seaboard. Income, welfare, and healthcare are indiscriminately sweeping through most of China. Such a conclusi",8.0,https://www.nature.com/articles/s41598-024-55129-y.pdf
ea7ac44025b51b998e8e1849f1430f680263e068,10.1177/23998083241281890,Radial analysis and scaling of housing prices in French urban areas,2024.0,Environment and Planning B Urban Analytics and City Science,Gaëtan Laziou; Rémi Lemoy; Marion Le Texier,"Urban scaling laws summarize how attributes evolve with city size. However, one limitation concerns the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is an important issue regarding housing prices, given their significant variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017–2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our approach with rescaled profiles fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than in the periphery. This has strong implicat",8.0,http://arxiv.org/pdf/2405.14503
7457bc8a205549e5a52525ecbcd4da46fff8ae79,10.1057/s41599-024-03244-6,Advanced modeling of housing locations in the city of Tehran using machine learning and data mining techniques,2024.0,Humanities and Social Sciences Communications,A. Pilehvar; Arian Ghasemi,"This research delves into the intricate dynamics of housing location in the bustling metropolis of Tehran. It aims to gain a deeper understanding of the factors influencing housing prices across the city. Employing a descriptive-analytical method, the study utilizes the Python programming language and its libraries, along with various regression models, to analyze a comprehensive dataset of 8000 villas and apartments spread across 22 districts and 317 areas. Data obtained from official sources are used to examine the correlation between housing prices and nine key determinants. The findings reveal strong positive correlations between the total value of the houses and several factors: surface area (80%), neighborhood location (75%), presence of an elevator (44%), presence of a parking lot (43%), and year of construction (26%), these demonstrate the importance of area and neighborhood. Conversely, the distinct number shows an inverse correlation (−41%) which means the higher the distinct number is, the lower the total value will be. In its final stage, the study employs cross-validation to evaluate the performance of various learning models, achieving a maximum accuracy of 85%. The r",8.0,https://doi.org/10.1057/s41599-024-03244-6
99edfd926ef38314f725da1ca82d06fbee89a1cd,10.1108/ijhma-07-2023-0094,Macro-economic indicators and housing price index in Spain: fresh evidence from FMOLS and DOLS,2023.0,International Journal of Housing Markets and Analysis,Ali Raza; Laiba Asif; Turgut Türsoy; M. Seraj; Gül Erkol Bayram,"
Purpose
This study aims to determine how changes in macroeconomic indicators and the housing prices index (HPI) are related. These factors can cause short-term and long-term changes in the housing market in Spain.


Design/methodology/approach
The study used cointegrating regression, fully modified ordinary least squares and dynamic ordinary least squares methodologies. The models are trained using quarterly time series data for these parameters from 2010 to 2022. A comprehensive examination is conducted to explore the relationship between macroeconomic issues and fluctuations in the HPI.


Findings
The results indicate statistically significant short-run effects (p < 0.05) of economic growth, inflation, Spanish stock indices, foreign trade and the interest rate on HPI. The inflation variables, Spain’s stock indices, interest rate and monetary rate, have statistically significant long-run effects (p < 0.05) on HPI. The exchange rate, unemployment and money supply have no substantial impact on HPI in Spain.


Originality/value
The study’s findings significantly contribute to increased information concerning the level of investing activity in the Spanish housing sector. After conduc",8.0,https://hdl.handle.net/11486/5419
60e607d4a12a46fe9b46a79601a17a5e6b6a94a8,10.7764/rdlc.22.1.163,Machine learning methods for prediction real estate sales prices in Turkey,2023.0,Revista de la Construcción,Cihan Çılgın; Hadi Gökçen,"Owning a house is one of the most important decisions that low and middle income people make in their lives. The real estate market is a significant factor of the national economy as much as it is important for individuals. Therefore, predicting real estate values or real estate valuation is beneficial and necessary not only for buyers, but also for real estate agents, economists and policy makers. This issue represents an active area of research, as individuals, companies and governments hold considerable assets in real estate. In this context, the aim of the study is to predict real estate prices with Machine Learning methods using the real estate sales data set in June and July 2021 belonging to the province of Ankara. In particular, it is to perform a comprehensive comparison on Machine Learning regression types methods that give successful prediction results in various but similar tasks, which are not included in the real estate literature. Real estate data obtained over the Internet was first included in a detailed data preprocessing process, and then Linear, Lasso and Ridge Regression, XGBoost and Artificial Neural Networks (ANN) methods were used on this dataset. According ",8.0,https://revistadelaconstruccion.uc.cl/index.php/RDLC/article/download/51473/49333
d8d2c984693866420298617808b5cba27b0ff952,10.1145/3645110,Improved Regression Analysis with Ensemble Pipeline Approach for Applications across Multiple Domains,2024.0,ACM Trans. Asian Low Resour. Lang. Inf. Process.,Debajyoty Banik; R. Paul; Rajkumar Singh Rathore; Rutvij H. Jhaveri,"In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our models outperform existing methods in terms of accuracy and reliability across all three datasets. The Pipelined Model, in particular, is notable for its ability to combine predictions from multiple models, leading to higher accuracy and impressive scalability. This scalability allows for their application in diverse fields like technology, finance, and healthcare. Furthermore, these models can be adapted for real-time and streaming data analysis, making them valuable for applications such as fraud detection, stock market prediction, and IoT sensor data analysis. Enhancements to the models also make them suitable for big data applications, ensuring their relevance for large datasets and distributed computing environments. It is important to acknowledge some limitations of our models, i",8.0,
e95ee4e1c94b218ae03c6065ec25688d7ce371eb,10.1371/journal.pone.0302631,The impact of housing prices and land financing on economic growth: Evidence from Chinese 277 cities at the prefecture level and above,2024.0,PLoS ONE,Qian Sun; Sohail Ahmad Javeed; Yong Tang; Yan Feng,"With the rapid progress of urbanization in China, the real estate industry, characterized by a long industrial chain, has become a pillar industry for economic development. Therefore, we inspect the nexus between land finance, housing prices, and economic growth. For this purpose, we use the panel data of 277 cities at the prefecture level or above in China from 2011 to 2019, and empirically examine it by using the Panel Vector Auto Regression (PVAR) model. The results show that there is a causal relationship between housing prices and economic growth. Housing prices promote economic growth in the short term and inhibit it in the long term. Both economic growth and housing prices have a significant impact on land finance. The economic growth show a significantly positive impact, while housing prices promote land finance in the short term with a long-term trend from positive to negative. This is the first study that tries to probe the relationship between urban housing prices, land finance, and economic growth by considering 277 prefecture-level and above cities in China. To promote the stable development of the regional economy, local governments need to overcome their dependence o",8.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0302631&type=printable
9b7dfbe34555ea53cb94fa4228900c4f65530761,10.3390/su11040984,Insufficient Consumption Demand of Chinese Urban Residents: An Explanation of the Consumption Structure Effect from Income Distribution Change,2019.0,Sustainability,Peng Su; Xiaochun Jiang; Chengbo Yang; Ting Wang; Xing Feng,"China’s consumption rate has continued to decline since 2000, which has retarded the sustainable growth of China’s economy. The dramatic changes in China’s income distribution have been very significant social characteristics, and they are also a very important factor for consumption. Therefore, this study analyzes the problem of insufficient domestic demand from the perspective of the effects of the income distribution changes on the consumption structure. The Almost Ideal Demand System model is improved by relaxing its assumption that expenditure equals income and giving it a dynamic form that includes the three characteristics of the income distribution evolution (the mean, variance, and residual effects) and measuring these. The results show that the mean effect is the largest one, and it basically determines the size and direction of the total effect. The variance effect is much smaller, but it may have some positive effects on the individual markets. The residual effect is the smallest and has a certain randomness. The income gap is not the main cause of the insufficient domestic demand. It is more likely to be caused by the decline of the mean effect, and the main driver of ",8.0,https://www.mdpi.com/2071-1050/11/4/984/pdf?version=1550144209
71ea609ea5158164fd5c87866f10e17e2fc6d696,10.1017/s0163548400003046,"Effects of the accident at Three Mile Island on residential property values and sales. Final report, April 1980-January 1981",1981.0,,H. B. Gamble; R. Downing,"The study examined the effects of the accident at Three Mile Island on residential property values and number of sales within a 25-mile radius of the plant. Regression analyses, using data on 583 actual market sales of single family homes from 1977 through 1979, examined the effects before and after the accident on the basis of distance and direction from the plant and on three different property value classes. All valid single family property sales between 1975 and 1979 within the 25-mile area were examined in a time series analysis. Interviews were conducted with realtors, financial institution officials and building contractors in the area. The accident had no measurable effects, positive or negative, on the value of single family residential properties within a 25-mile radius of the plant, or in any direction from the plant, or on low, medium, or high value properties. The plant had no measurable effects on residential property values for the 2 years prior to the accident. Immediately following the accident there was a sharp decline in the number of residential sales within 10 miles of the plant, but the real estate market returned to near normal conditions within 4-8 weeks. Th",8.0,http://ageconsearch.umn.edu/record/159815
368d89363becbf5e5b737e6b7a6eb0b2b4cb2167,10.1108/ijhma-04-2023-0050,Housing market shifts favouring transit-oriented development in emerging economies: the link between metro rails and housing price dynamics in Delhi,2023.0,International Journal of Housing Markets and Analysis,V. Agnihotri; S. Paul,"
Purpose
This paper aims to understand the spatiotemporal influence of metro rail connectivity on housing prices in surrounding areas. The study assesses the average annual price shift for apartments around metro stations in Delhi during the previous decade, specifically from 2010 to 2019. The authors examine the spatiotemporal extents to which housing prices are determined by the prominence of metro stations and spatial development around metro stations.


Design/methodology/approach
The authors perform the cross-tabulation analysis to calculate chi-square values to test the hypotheses concerning the responsiveness of the housing market in Delhi to the number of locational variables in the areas connected with the mass public transportation system.


Findings
The empirical findings verify the existence of a housing market overvaluation in Delhi around metro stations until 2013, which was eventually re-adjusted after 2014. The key findings of the study suggest the role of location variables concerning metro rails in the shooting up of the housing prices in the city. In addition, the research establishes the association of annual housing price shifts to the metro rails in the short-",8.0,
e530007093eb1edb30dcd0e1a2d11885b95fc490,10.3390/su12229384,Dynamic and Heterogeneous Demand for Urban Green Space by Urban Residents: Evidence from the Cities in China,2020.0,Sustainability,Tianzheng Zhang; Yingxiang Zeng; Ying-Jie Zhang; Yanjiao Song; Hong-Xun Li,"With the increasing number of people starting to attach importance to urban green space (UGS), estimating urban residents’ demand for UGS has become critical. Previous studies have estimated residents’ demands for UGS in different cities using the Rosen–Roback model. However, there has been little analysis of the renters’ requirements for UGS or the dynamic and heterogeneous demands for UGS by residents in cities with varying characteristics. In this study, the prefecture-level cities in China were selected as the object of study for their wide representation of various city characteristics, and the Rosen–Roback model was used to address the abovementioned issues. Residents’ demand for UGS in 285 prefecture-level cities was assessed from 2010 to 2017. The results confirm our hypothesis that when UGS increases by 1%, house prices will increase by about 3% and rents will increase by about 7%, and the demand continues to grow. We also analyzed the heterogeneous demands for UGS by urban residents in different types of cities from three aspects: population density, economic level, and the effects of the National Forest City policy. Finally, we provided suggestions based on empirical res",8.0,https://www.mdpi.com/2071-1050/12/22/9384/pdf?version=1605165927
9582ac16e38a851c6bacf85719b5525cd2e4833a,10.24963/ijcai.2018/363,Generalization-Aware Structured Regression towards Balancing Bias and Variance,2018.0,International Joint Conference on Artificial Intelligence,M. Pavlovski; Fang Zhou; Nino Arsov; L. Kocarev; Z. Obradovic,"Attaining the proper balance between underfitting and overfitting is one of the central challenges in machine learning. It has been approached mostly by deriving bounds on generalization risks of learning algorithms. Such bounds are, however, rarely controllable. In this study, a novel bias-variance balancing objective function is introduced in order to improve generalization performance. By utilizing distance correlation, this objective function is able to indirectly control a stability-based upper bound on a model's expected true risk. In addition, the Generalization-Aware Collaborative Ensemble Regressor (GLACER) is developed, a model that bags a crowd of structured regression models, while allowing them to collaborate in a fashion that minimizes the proposed objective function. The experimental results on both synthetic and real-world data indicate that such an objective enhances the overall model's predictive performance. When compared against a broad range of both traditional and structured regression models GLACER was ~10-56% and ~49-99% more accurate for the task of predicting housing prices and hospital readmissions, respectively.",8.0,https://www.ijcai.org/proceedings/2018/0363.pdf
d48b10b5b95f48c87d506c8f9cc3531dd06f3cfc,10.2166/wcc.2019.044,A review of climate change (floods) and economic attributes response to residential property value in Malaysia,2019.0,Journal of Water and Climate Change,Siti Hafsah Zulkarnain; M. Tsujimura; M. A. Yuzir; M. N. Razali; Z. Tarmidi,"
 The art and science of determining residential property value has evolved due to the changing external factors, such as the economy, environmental (climate change) and social aspects. This research aims to identify the impact of climate change (floods) to determine residential economic attributes that could affect the value for residential property in flood risk areas. The case study covers all residential housing schemes in Langat River Basin area, which has been considered as the highest flood risk area in the State of Selangor, Malaysia. The methodology of this research is based on the conceptual analysis from previous studies from local and international scenarios. The systematic analysis of previous literature of real estate valuation theory consists of economic attributes such as structural, locational and environmental attributes involved in residential property valuation in relation to flooding. The findings reveal that the economic attributes' response to flood hazards for residential properties can be divided into three conditions, and they are: positive, negative or no effect on the climate change factor.",8.0,https://iwaponline.com/jwcc/article-pdf/11/4/1084/829616/jwc0111084.pdf
2f0469272b61f0d60fb03fe98e2eff983e4d5235,10.3846/1648715x.2015.1031854,Momentum and mean reversion in regional housing markets: evidence from variance ratio tests,2015.0,,Elias Oikarinen; Felix Schindler,"This study adds to the literature on mean aversion and mean reversion in housing prices. In contrast with the previous related literature, the persistence and reversion characteristics are studied by computing variance ratios using Kim's (2006) Wild bootstrapping and by investigating horizons up to 10 years. The variance ratios clearly indicate that housing prices do not follow random walk in any of the 15 Finnish cities included in the analysis. Instead, momentum in housing price growth is longlasting and considerable in size. Since the eventual reversion is substantially weaker than the initial mean aversion, housing is notably riskier asset in the long term than suggested by variances computed from quarterly or annual price movements. The results also show that the momentum and reversion patterns may substantially vary between regional housing markets. These differences influence the optimal housing portfolio allocation and highlight one more reason why it is complicated to use country level housing price data when analyzing the optimal portfolio allocation or housing price dynamics.",8.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/2122/1714
68571666eceaf7de33a4aaebfdfaadb0fee99473,10.5018/economics-ejournal.ja.2018-56,Bobos in paradise: urban politics and the new economy,2018.0,Economics,G. Saint-Paul,"Abstract This paper provides some elements to explain the observed takeover in some urban areas of a new kind of elite associated with new economy jobs, also known as “bourgeois bohème” (bobos). This takeover has been associated with greater investment in urban amenities and “clean” means of transport, with adverse effects on commuting time. The model allows us to explain those developments by productivity growth in the new economy, and by the differences in production processes between the new and old economies. The consequences of bobo takeover for house prices and employment of unskilled service workers are also discussed. A bunkerized equilibrium in which skilled workers in the old economy no longer reside in the city and have been replaced by service workers is studied. In such an equilibrium urban amenities are at their maximum and commuting flows have been eliminated. For some parameter values, bobos are better-off under bunkerization, in which case they may gain by favoring it with a “diversity” subsidy for unskilled workers to reside in the city.",8.0,https://www.degruyter.com/document/doi/10.5018/economics-ejournal.ja.2018-56/pdf
db11ff372cd0bdb5bf6dafc5577d20db576cb68a,10.54660/.ijmrge.2025.6.1.1089-1094,Green Buildings and Sustainability: A Review of Economic and Environmental Impacts,2025.0,International Journal of Multidisciplinary Research and Growth Evaluation,Ahmad Abdullah Baeroom; Md. Faisal Hossen; Muhammad Aziz Abid; Faiza Shahzad,"A key tactic in combating climate change, energy efficiency, and sustainable resource use is the use of green buildings. Green buildings, which are distinguished by increased water and energy efficiency, less environmental impact, and higher occupant comfort and health, provide substantial advantages at both the individual and metropolitan levels. These include greater transaction and occupancy rates, reduced operating expenses, higher property prices, and wider societal benefits including market expansion and employment creation. Despite these advantages, green buildings are still not widely adopted, especially in the residential sector. Research has emphasized the significance of comprehending consumer behavior, showing that, regardless of cultural or geographical variations, economic, environmental, and socio psychological aspects have a significant impact on the intention to invest in residential green buildings. Additionally, it has been demonstrated that sustainability characteristics in real estate development have a major impact on property values, which is why industry participants and governments must include these elements in their strategy. Evidence also points to a com",8.0,
dac42d8673b6a8e4d8a1f68472f4c7c4261d0aef,10.3390/su14159107,Intermediate Effect of the COVID-19 Pandemic on Prices of Housing near Light Rail Transit: A Case Study of the Portland Metropolitan Area,2022.0,Sustainability,Sang-Wan Lee; Liming Wang,"This study explored the dynamics of a residential property value premium for proximity to a light rail transit (LRT) station in the intermediate term (roughly two years) since the pandemic. We applied a longitudinal quasi-experimental design using repeat sales data from the Portland Metropolitan Area, Oregon. Our results indicate that the effect of the pandemic on prices of housing near LRT stations differs between single-family and multi-family markets. Since the pandemic outbreak, there has been no statically significant difference in the price appreciation between single-family (SF) housing within an LRT service area and otherwise similar SF homes; however, for multi-family (MF) homes, those within an LRT service area have experienced a 3.0% lower price appreciation rate than MFs outside such areas with similar characteristics. Our findings help better highlight the impact of the pandemic on the real estate market and can inform discussions about longer-term changes in post-COVID cities and their planning.",8.0,https://www.mdpi.com/2071-1050/14/15/9107/pdf?version=1658754320
55a91319a22eb3e7a2f06a01cae53af03a58a5d0,10.24191/abrij.v5i2.10003,Determinants of Household Debt in Malaysia from the year 2010 to 2017,2019.0,ADVANCES IN BUSINESS RESEARCH INTERNATIONAL JOURNAL,Zahariah Mohd Zain; Nurul Ainun Ahmad Atory; Sarah Hanafi,"Household debt has become an issue in the Malaysian economy as it affects the country socially and economically.This study aims to examine the determinants of household debt from the year 2010 until 2017. This study employs the Ordinary Least Square (OLS) method and the macroeconomic variables used in this study are Gross Domestic Product (GDP), base lending rate, unemployment and housing price as independent variables. The results indicate that the trend of household debt in Malaysia has shown a continuous rise from the year 2010 to 2017. GDP, base lending rate and housing price indicate a positive relationship towards household debt while unemployment shows a negative relationship to household debt in Malaysia. All explanatory variables have shown a significant relationship except for GDP. Housing price has been found to be the most significant factor and positively related to household debt. The findings indicate that the higher the price of houses, the higher the household debt will be.",8.0,https://doi.org/10.24191/abrij.v5i2.10003
a0bfc11da36881f3707e41c261d5d0fc39eb1af1,10.1108/ijhma-12-2021-0138,Panel smooth transition regression approach to analyzing tourism development – housing price nexus in G7 countries,2022.0,International Journal of Housing Markets and Analysis,Li Yang; A. Khaskheli; S. Raza; Nida Shah,"
Purpose
The purpose of this study is twofold: first is to examine the nonlinear relationship between tourism development and housing prices, and secondly, the role of economic growth in facilitating the materialization of tourism development and housing prices nexus in G7 countries.


Design/methodology/approach
The authors used the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data from 1995 to 2018.


Findings
Results confirmed that the nexus between the tourism development and housing prices is nonlinear and regime dependent. Moreover, the results showed that the threshold level of economic growth above which tourism development increases the housing prices is 2.63%. The relationship above the threshold value is positive and growth enhancing, while below the threshold, tourism development has a negative effect on housing prices. The economic growth and housing prices also showed the U-shape relationship implying that at a certain level increase in economic growth decreases the housing prices but after a certain level increase in economic growth increases the housing prices.


Originality/value
This paper ",8.0,
19f89e4cac98ea778d883c2ec649f6489d3c64ad,10.1177/0958305x211056031,Spatial analysis of public residential housing's electricity consumption in relation to urban landscape and building characteristics: A case study in Singapore,2021.0,Energy and the Environment,Hui Yun Rebecca Neo; N. Wong; M. Ignatius; C. Yuan; Yong Xu; K. Cao,"In a highly populated country like Singapore, a significant percentage of our gross annual electricity consumption stems from our domestic electricity usage in our residential houses. Analyzing and understanding factors that could influence such patterns is thus essential in order to derive effective measures to reduce usage. In this research, 16 identified variables were calculated and considered in the spatial analyses based on various buffer sizes. Both multilinear regression (MLR) and geographically weighted regression (GWR) based analyses were conducted using each residential housing's Energy Unit Intensity (EUI) as the dependent variable. The analyzed results have shown that building characteristics variables have more significant influences towards energy consumption patterns as compared to urban landscape variables. Although little difference was observed across different buffer sizes, more reliable results were obtained from a smaller buffer size of 50 m, suggesting its suitability in using these obtained values for further prediction model analysis and development. Results obtained from the GWR-based analysis have shown a significant improvement in the goodness-of-fit val",8.0,
a3acc7347c71d0b395547d107c76828791f5a784,10.2139/ssrn.3733105,Are Historic Districts A Backdoor for Segregation? Yes and No,2022.0,Social Science Research Network,Jamie Bologna Pavlik; Yang Zhou,"Historic districts preserve the heritage of designated areas and increase the amenity for residents. However, these districts also come with restrictions and increased housing prices that could cause segregation. We study how two historic district programs impact residential segregation in Denver. We find that homebuyers are more likely to be White within historic districts, but that the official historic designation has no effect on this probability. More specifically, we calculate that the predicted probability of having a White homebuyer increases from 77 to over 80 percent when the home is located within a historic district. Similarly, we find that most transactions flow from White sellers to White buyers, regardless of official designation. Thus, while historic districts tend to be more segregated, official designation does not seem to amplify this existing problem. JEL Codes: R20; R30; J15",8.0,https://doi.org/10.2139/ssrn.3733105
3c69ae567ab44b836ebe8f444043329e4120b250,10.5539/ijms.v3n2p64,"Price Effects of Landfills on Residential Housing in Lagos, Nigeria",2011.0,,O. A. Akinjare; S. Oloyede; C. Ayedun; C. O. Oloke,"The question as to whether solid waste landfills affect residential property values has long been a subject of controversy and debate. Previous studies have resulted in mixed conclusions. This study examines four landfill sites in Lagos which differ in size, operating status and history. The relationship between each landfill and property values were measured based on distances of 1.2km radii away from the landfill locations and the measurements were based on the interval of 300meters up to 1 200 meters in concentric rings. The result of the study indicated that across the four landfill sites, increase in property values were evident as distance away from the landfills increased indicating that residential houses in close proximity to the landfills suffered value loss. Property appreciation relative to distance averaged 5.75% within the concentric rings for all four landfills.",8.0,https://ccsenet.org/journal/index.php/ijms/article/download/10376/7401
4ec88bbd30e6ba3d300bcff088e97e35f2eef95c,10.1080/10168737.2019.1610901,The Impact of Monetary Policy and Housing-Purchase Restrictions on Housing Prices in China,2019.0,International economic journal,Sherry Yu; Li-Ni Zhang,"ABSTRACT This paper examines the level and volatility effect of monetary policy on housing prices in China utilizing a novel set of housing price indices constructed by (Fang, H., QuanlinGu, W. X., & Zhou, L.-A. (2015). Demystifying the Chinese housing boom. NBER Macroeconomics Annual 2015, Volume 30. University of Chicago Press.). We find that in the long-run, average housing prices react positively to inflation, money supply and bank lending growth, and negatively to the reserve requirement ratio and benchmark lending rate. Housing prices in Tier 1 cities respond more sensitively to monetary shocks relative to Tier 2 and 3 cities, possibly due to surging demand and limited supply under housing-purchase restrictions (HPR). We further study the volatility effect of monetary shocks using the GARCH model and find that the benchmark lending rate, reserve requirement ratio and money supply growth have strong negative impact on the volatility of housing price growth. Our benchmark results remain robust after incorporating the HPR policy variable in the estimation, with a significant negative effect of HPR on housing price growth in Tier 1 and Tier 2 cities. Lastly, we conclude with reco",8.0,
c452517d9fb1e150b6044696f3f5808d238d1e71,10.15208/beh.2017.23,Demand-side factors of housing price increases in Turkey: Blanchard-Quah SVAR model,2017.0,,Hülya Deniz Karakoyun; Nurtaç Yıldırım,"A bubble in the housing sector is defined as an unprecedented upsurge in house prices which cannot explained by fundamental determinants of the housing sector. This study examines demand-side factors of real estate sector in Turkey in order to expose whether house price increases in the country can be counted as a bubble. We use the Blanchard-Quah SVAR model to empirically reveal the relationships between the real mortgage interest rates, house price gap, price-rent-ratio and purchasing power parity. The results of the paper indicate that real interest rates and other variables have a significant explanatory power in the long run whereas house prices cannot be explained by these variables in the short run. Therefore it can be asserted that the house price increases in Turkey cannot be evaluated as a bubble in the long run.",8.0,http://ageconsearch.umn.edu/record/264702
3cc62df1c7d0ec4fa5a7c9163e0abe59d2cea643,10.1109/icetet-sip58143.2023.10151561,Real Estate Prediction System Using ML,2023.0,2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP),Abhinav Wandhe; Lakshya Sehgal; Hardik Sumra; Aryaman Choudhary; M. Dhone,"In recent years, machine learning has played a significant role in many aspects of our lives, including medical diagnosis, natural speech command, picture detection, product suggestion, spam recognition, and price prediction, etc. The desire to receive a profit on an investment property is a typical justification for home buying. They frequently want to know when and where to buy a property, thus they frequently ask the same questions. Current Real Estate Management System does not provide the prediction of the property price for users. In this project we offer the facility to the users to look for properties. This will provide the facility to view the system as an admin or user. The unique feature of this system is that it can predict the chance of a rise in the price of a property in the coming future. Prediction is done with the help of Machine Learning Algorithms such as Convolutional Neural Network (CNN) and Natural Language Processing (NLP), which helps to generate an output that is either positive (1) or negative (0). The predicted value depends upon various factors of the property such as property area, surrounding area, age of construction, Floors, Available rooms and carp",8.0,
c59c73d4433eae0b582bc5cf0dcf71c493e1941c,10.48550/arxiv.2405.07456,Boosting House Price Estimations with Multi-Head Gated Attention,2024.0,Expert systems with applications,A. Sellam; C. Distante; A. Taleb-Ahmed; P. L. Mazzeo,"Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all dataset",8.0,
4fbe553cfb0b2600eaf110898ac72714b198292d,10.1108/dta-05-2020-0111,Using photographs and metadata to estimate house prices in South Korea,2020.0,Data Technologies and Applications,Changro Lee; Key-Ho Park,"PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that",8.0,
753e008eca6d2ef0da8b9dcdb342bc891c3b3881,10.29259/jep.v14i2.8813,Analisis faktor yang mempengaruhi permintaan kredit pemilikan rumah (KPR) di Kota Palembang: Kasus nasabah KPR Bank BTN,2016.0,Jurnal Ekonomi Pembangunan,Deri Sandria; Nazeli Adnan; Saadah Yuliana,"The purpose of the study was to investigate the effect of income, house prices and interest rates on the demand for housing loans (KPR) at BTN Bank in Palembang city. This study uses secondary data and primary data. The method used is a quantitative approach by applying a linear regression model. The findings from the results of the study indicate that variations in income variables, house prices and interest rates explain the variable demand for mortgages by 57.6%. In addition, another finding from this study is that the income variable has an insignificant influence on the demand for mortgages, while the variable house prices and interest rates have a positive and significant influence on the demand for mortgage loans in Palembang city.",8.0,https://doi.org/10.29259/jep.v14i2.8813
2803b0b59ff3896da756d3ac3247eb17b1d40ad9,10.54254/2755-2721/4/2023362,House price prediction based on machine learning,2023.0,Applied and Computational Engineering,Hanwen Li,"Machine learning is commonly used in the real estate market. It is vital to apply the idea of machine learning in this field to predict house prices based on various features. The paper will focus on how to use the most appropriate machine learning models for house price prediction. It will use LightGBM(Light Gradient Boosting Machine), Gradient Boosting, and XGBoost(Extreme Gradient Boosting) to train models to predict house prices using the existing data from the Kaggle website. After three models make predictions, they will get an RMSE (root mean square error), whichis0.02975, 0.02537, and 0.01364. Based on the result, the XGBoost model is the best one among these three models used for house price prediction.",8.0,https://www.ewadirect.com/proceedings/ace/article/view/2092/pdf
7321c2939fc9f5f082a50632c78886893dfe4d64,10.1109/bigdataservice49289.2020.00034,ZenDen - A Personalized House Searching Application,2020.0,International Conference on Big Data Computing Service and Applications,Kristina Milkovich; Saurabh Shirur; Pratap Desai; L. Manjunath; Wencen Wu,"A method directly linking buyers and sellers in the housing market would benefit both parties in both the renting and purchasing domains. The real estate mobile applications available today, such as Zillow and Redfin, are highly dependent on filter based searches (location, price, bedrooms, etc) and contain many screens that require zooming in and out of map-based interfaces. Furthermore, the student market and lower-income demographics that are more likely renting a house instead of buying are usually not the focus of these applications. Described in this paper is a novel mobile application that will change the way people in the renting market, e.g., university students, find lodging. The implementation contains a streamlined swipe-based user interface backed by a user-house recommender system to manage content. Deep learning techniques are used in building the recommender system that recommends houses to users based on their view history. For image classification, we build convolutional neural networks (CNN) for analyzing house images. The goal is to create a personalized, easy to use application that will reduce the effort and time required for people in the renting market to fi",8.0,
4aa212dc4d750f529d65e957b96ec2b893984391,10.1016/j.resourpol.2020.101777,"Impacts of mining on property values in Kalgoorlie-Boulder, Western Australia",2020.0,,Chomba Kolala; M. Polyakov; James Fogarty,"Abstract Mining activities have a positive impact on local communities through providing formal employment opportunities in the local area. However, the air and noise pollution associated with surface mining represent a negative externality for residents in communities near mining activity. To support urban planning processes, accurate information on both the positive and negative impacts of surface mining is required. In this study, we estimate the dis-amenity of proximity to a large open-pit gold mine in Kalgoorlie, Western Australia, that has been capitalised into residential property values. To estimate the impact of proximity to the mine on property values, we use sales data for 21,850 residential properties sold in Kalgoorlie-Boulder between 1990 and 2018. We use distance to the mine as a measure of the impact. We control for spatial heterogeneity using spatial fixed effects. We found that residential properties located within two kilometres of the mine trade at a 20% to 30% discount to similar residential properties located at least six to seven kilometres from the mine. Our results can be used for planning appropriate buffer zones around mining activities.",8.0,
e217681a0c6a91107aa1d6f31181952f096a4892,10.1109/globconpt57482.2022.9938255,Selection of Data Preprocessing Techniques and Its Emergence Towards Machine Learning Algorithms using HPI Dataset,2022.0,"2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT)",G. V. Sai Krishna; Kundrapu Supriya; K. Rao,"To compute the frequent metamorphosis of the housing price, the House Price Index (HPI) is one of the effective indicators. Various methodologies are involved in the data processing of the current house prices, which are affected by multiple factors like house configuration, building class, air conditioning quality, etc. Remarkably, a greater number of research papers adopting classical machine learning approaches are introduced to estimate house sale prices accurately. Still, they barely regard the data processing techniques that make the data suitable for modelling more accurate house price forecasting models. This research contributes to a wide variety of adequate data preprocessing. It extensively highlights mechanisms like missingness of data, missing data handling, outliers, and feature scaling extensively to build efficient predictive models. Compre-hensive arguments have been broadly presented to portray the advantages and disadvantages of prevailed data preprocessing techniques at various distribution scenarios of variables in the house price data. The current research conclusions oblige the evolution of modern data-driven research in machine learning.",8.0,
aa1db096839f21bd22ceea96832bc3c5ebbc7503,10.1177/2321022220980541,The Asymmetric Effects of Regional House Prices in the UK: New Evidence from Panel Quantile Regression Framework,2021.0,Studies in Microeconomics,Panayiotis G. Tzeremes,"This study unfurls the non-linear behaviour of regional house prices in the United Kingdom by employing quarterly observations spanning the period 1992Q1–2017Q4. Our enquiry aims at examining UK house prices within a multivariable framework and, more specifically, by employing panel quantile regression with fixed effect. In brief, the empirical findings obtained from these methodologies indicate that the UK house prices are influenced at lower and upper quantiles, and that precisely they are influenced by variables such as income, private sector housing starts and employment. We highly support that there is a strong heterogeneity among UK regions and that asymmetry may be one of the keys of the ripple effect. Particularly, the income shows a positively significant performance at lower and higher regional house prices. Moreover, the variables private sector housing starts and employment rate are statistically significant for house prices. Leveraging for first-time panel quantile regression for the case of regional house prices in the UK, policymakers will have a profound understanding of regional house prices. JEL Classifications: C22, R21, R31",8.0,
aa901021244f8337ac98abd82faf5d4acdd1fcc4,10.1080/19491247.2023.2178819,Moving up the social ladder: homeownership and expected socioeconomic status among migrant young adults in China,2023.0,International Journal of Housing Policy,Yin-Xin Su; Mingzhi Hu; Xiao-Feng Yu,"Abstract A substantial body of literature on the promotion of socioeconomic mobility has focussed on the role played by government support and human capital investment. However, homeownership as a channel for upward socioeconomic mobility has been under-researched. This study investigates the relationship between homeownership and expected socioeconomic status by using data from a self-conducted survey in 2021 on migrant young adults in China’s five superstar cities (i.e., Beijing, Shanghai, Guangzhou, Shenzhen and Hangzhou) where housing prices have been soaring in the past decade. Results indicate that homeowners expect higher socioeconomic status 5 years later than renters. This effect cannot be explained by household characteristics, such as current socioeconomic status, income and education, and occupational characteristics, such as job positions and work units. Explanations from the perspective of asset appreciation and social integration are examined for this effect. Further analyses show a larger effect of homeownership for those who own at least two homes, with a college degree or above, or with a mortgage loan.",8.0,
86bc824e728f103959f1271e9c7d80f7bb6b0940,10.1080/02673037.2021.1929858,Residential property in Australia: mismatched investment and rental demand,2021.0,Housing Studies,M. Yanotti; Danika Wright,"Abstract Housing prices in Australia have demonstrated strong growth in recent decades, and many argue housing supply is not keeping up with the demand. The Australian government purports to increase the private construction of new houses and availability of rental housing primarily through taxation offsets. However, inflated house prices are also at least partially explained by housing supply shortage. This work studies Australian residential property investors to understand their characteristics and role in contributing to the supply of rental housing. Using rich proprietary loan-level data on over 1.1 million mortgage applications during a period of stable policy and house price appreciation, we study the determining factors for accessing finance for the purpose of residential investment as opposed to owner-occupation. Our findings use historical data to present new evidence of the increasingly non-metropolitan location choice for real estate investment properties. This is a potential explanation for the shortage of suitable housing in metropolitan regions but may contribute to regional development.",8.0,
de0239452a57208e059bb1608e1619de4a185551,10.1002/ijfe.1725,A tale of two shocks: The dynamics of international real estate markets,2019.0,International Journal of Finance and Economics,S. Bekiros; Amanda Dahlström; G. Uddin; Oskar Ege; R. Jayasekera,"We examine the major potential drivers of five international housing markets utilizing a quantile regression approach. In particular, we investigate property market dynamics during three variant market environments, namely, under downward (bearish), normal (median), and upward (bullish) trending conditions. Monthly data series for the United States, United Kingdom, Australia, Singapore, and Hong Kong are analysed, in an attempt to quantify uncertainty and detect trading patterns for the largest securitized real estate markets. We find that the stock market volatility, measured by the “pushing factor” VIXS&P500, provides agents with the most reliable and efficient information in terms of predicting market returns during bear market conditions, whereas “pulling factors” such as money supply, treasury yields, and unemployment explain the main stylized facts, incorporating contagion and diverse endogenous and exogenous shocks. Our work provides a richer understanding on comovements in house prices, allowing policy makers to anticipate shocks in global markets in a timely manner.",8.0,
c7e0820fd763d766e5b0f97c595397f55fbb85a0,10.1515/jbnst-2020-0043,The Macroeconomic Determinants of House Prices and Rents,2021.0,Jahrbücher für Nationalökonomie und Statistik,Jakob Shida,"Abstract Based on panel error correction models for a sample of up to 21 countries, this paper analyses the macroeconomic determinants of house prices and rents. In accordance with the existing literature, I find significantly positive effects of per capita income and bank lending on house prices, whereas the housing stock per capita and interest rates have negative effects. For rents, the results are somewhat more remarkable, indicating that both the housing stock and interest rates have a negative effect. While contradicting conventional economic theory, the latter finding might be explained by real estate investors exploiting their pricing power with varying degree depending on the level of real interest rates. Moreover, the estimated impact of interest rates on both house prices and rents varies with structural housing market characteristics. For instance, while interest rates have a more pronounced effect on house prices in countries with more developed mortgage markets, the same does not hold for the effect of interest rates on rents.",8.0,
c6e7269e7317d8bcf58ba4c8038dfbfba1315727,10.3390/land10030261,More Reliable Land Price Index: Is There a Slope Effect?,2021.0,Land,Yi Huang; G. Hewings,"This paper focuses on the physical attributes of land that intrinsically limit land use and possibly affect land values. In particular, we investigate if the slope of a land does decrease its price and investigate the role of land slope in forming more reliable constant-quality land price indices and aggregate house price indices. We find that, while land slopes do decrease the land price per unit, they have a small effect on the quality-adjusted land price indices in selected neighborhoods in Auckland, New Zealand, where sloped terrain is common.",8.0,https://www.mdpi.com/2073-445X/10/3/261/pdf?version=1615891884
53b656618e08abbff36bab7feffd8db6f6fecf78,10.1145/3219104.3229253,Investigation of Florida Housing Prices using Predictive Time Series Model,2018.0,Practice and Experience in Advanced Research Computing,Temilola Aderibigbe; H. Chi,"In the ever-changing1 real estate market, there are certain factors that have a huge impact on the fluctuating of house prices, and there are relationships between those factors. The only way to understand the behavior of the changes would be to explore historical data of those factors that impact the housing prices most. Many research papers have studied to predict the trends of house prices, in various countries, using various statistical models, and machine learning methods. Florida housing prices not only depend on various economics indexes but also impact by other natural disasters factors such as hurricane etc. which will be considered in future research. This project will involve the study of the housing market in Florida; we will be considering various factors such as the number of sales, employment rate, interest rate, GDP, inflation rate, hurricane strikes etc. Dataset from 1985 to 2016 will use in our analysis and case study and the time series is adopted. The prediction of this model is promising.",8.0,
648173da08df87c7dcc86b091286011e9f938508,10.1080/20954816.2019.1697535,"The development of Hong Kong housing market: past, present and future",2020.0,,T. Chong; Xiao-Yang Li,"Abstract From previous boom-bust cycles of the housing market of the Hong Kong SAR of China, we find that the imbalance between housing demand and supply, the interaction between the stock market, financial institutions and the real estate market, and international capital flows are the key determinants of housing price movements in Hong Kong. A comparison between the current episode of high real estate prices and that in 1997 suggests that structural changes have taken place and that different underlying forces are in play, thus indicating a low likelihood of a similar crash in the housing market. However, it is still imperative for the Hong Kong SAR government to find effective ways to cool down the overheated housing market. While we agree that the actions taken by the government will be able to suppress the housing price in the long run, the short-term effect may not be satisfactory due to the time lags of these policies.",8.0,
b15642f7afd94a080fb58d7b7f007458e937ef80,10.1080/00036846.2020.1733473,Speculative trading in Chinese housing market: a panel regression method,2020.0,,Zhen-Xi Chen; Cun-Tong Wang,"ABSTRACT The major Chinese cities experienced dramatic increases in their house prices in the recent years. This paper derives the fundamental value of the housing markets based on the personal disposable income of individual cities. By controlling macroeconomic variables and government intervention, we detect speculative trading based on fundamental value and historical price movements. Fundamentalists expect house prices to converge to the fundamental values while chartists hold a momentum trading expectation. Further differentiating the cities into tier-1 and non-tier-1, the non-tier-1 cities are found to be subject to a risk of plummeting arising from the interaction terms between the fundamentalists and chartists.",8.0,
f26be5616b0eb9e906c42d88c81d8459ebcaabbb,10.1177/15270025221132221,Sports Facilities as a Housing Amenity: Do Prices Follow Facilities?,2022.0,Journal of Sports Economics,Geoffrey Propheter,"The sports facility amenity theory predicts that when facility sites change, home prices nearer the prior site decline while home prices nearer the new site increase. Using home sales data in the vicinity of two proposed facility sites for the Golden State Warriors in San Francisco, the data are generally supportive of the amenity theory. The study also shows that Euclidean and walking distance can yield dissimilar treatment effect sizes, thus compelling researchers to justify their distance measurement choice. Finally, the data provide weak support that home buyers respond to changes in facility re-designs towards increased public benefits.",8.0,
4d6490850634808b04e25e82bd46f7e11efe2955,10.1007/978-3-031-34107-6_23,Comparing Machine Learning Techniques for House Price Prediction,2023.0,Artificial Intelligence Applications and Innovations,K. P. Fourkiotis; A. Tsadiras,,8.0,https://inria.hal.science/hal-05498067
b7236dafbd8ab8a4e6e6ba18bd114f4f46e2511a,10.1111/j.1467-940x.2009.00160.x,SPATIALLY‐VARYING EFFECTS OF REZONING ON HOUSING PRICE,2009.0,,Seong-Hoon Cho; S. Kim; D. Lambert,,8.0,
c02ca0c54d59f80228e7f3d36c848a26da1ed15d,10.1007/s10901-020-09805-w,"City proximity, travel modes and house prices: the three cities in Sydney",2021.0,Journal of Housing and the Built Environment,S. Herath; A. S. Jayasekare,,8.0,
99dc9aaf3fe332f13e65b748d3a9d4d72cc37826,10.26700/shuri.2018.08.8.2.39,Prediction of Seoul House Price Index Using Deep Learning Algorithms with Multivariate Time Series Data,2018.0,SH Urban Research & Insight,Tae H. Lee; M. Jun,,8.0,
74beea8fa6f43d6193c5a245bd33b399c7b29d33,10.1111/jors.12207,Hedonic Pricing When Housing is Endogenous: The Value of Access to the Trans‐Israel Highway,2016.0,,M. Beenstock; Daniel Feldman; D. Felsenstein,,8.0,
aecafc266bf405f8f9319d72cb455ebd223d7ae2,10.61093/fmir.8(3).227-253.2024,Forecasting Trends in the Real Estate Market: Analysis of Relevant Determinants,2024.0,Financial Markets Institutions and Risks,Olena Dobrovolska; Nazar Fenenko,"The real estate market in Ukraine, particularly in Kyiv, has experienced significant fluctuations due to the ongoing conflict and associated economic challenges. This research focuses on forecasting trends within the market, with a specific emphasis on price dynamics and the factors influencing these shifts. The actuality of this study stems from the pressing need to understand how the war, macroeconomic instability, and demographic changes impact real estate demand, investment patterns, and property prices. Given the rapid shifts in market dynamics and the complex interplay between economic variables, accurate forecasting is crucial for stakeholders such as investors, policymakers, and analysts. The methodology of this study combines traditional and advanced analytical tools to provide comprehensive and accurate forecasts. A dual-approach forecasting model was employed, using both the Brown-Mayer method implemented in Excel and advanced functionality provided by Statgraphics software. The Brown-Mayer method, which relies on exponential smoothing, allowed for the analysis of basic trends and seasonality in the time series data. Statgraphics, with its capacity to consider complex ch",7.0,https://doi.org/10.61093/fmir.8(3).227-253.2024
098798e4f4c2bb78e7a8cc2618945368f70ed31d,10.1088/1757-899x/471/11/112074,Effectiveness of Applying Marketing Tools in Real Estate Trading,2019.0,IOP Conference Series: Materials Science and Engineering,Magdalena Belniak; E. Radziszewska-Zielina,"The aim of this paper is to evaluate the effectiveness of marketing tools used by real estate agents in Krakow. Based on a diagnostic survey and questionnaire surveys, as well as subject of article sources, a cross-sectional analysis of various marketing tools, the frequency of their application and the effectiveness of marketing activities was performed. The collected empirical material and analysed literature allowed the formulation of the following conclusions: a) the most commonly used form of real estate promotion is online advertising and the use of classified advertisement websites, b) social media play a big role and its importance will be constantly growing allowing for the effective reaching of targeted groups of customers, c) modern methods of real estate visualisation are not practiced by real estate agencies in Krakow, despite the fact that certain benefits for entities implementing them have already been recognised the need to prepare high-quality photographic descriptions of properties in the case of preparing marketing offers has been noticed. The essence of real estate marketing in recent years has changed radically. Until recently, customers searching for real est",7.0,https://iopscience.iop.org/article/10.1088/1757-899X/471/11/112074/pdf
bfc3d7e5957b98be10250dd51d4a0cf882e1bb3a,10.1108/pm-06-2018-0038,Determinants of Indian housing market: effects and counter-effects,2019.0,,Richa Pandey; V. Jessica,"The purpose of this paper is to study the effect of the 2008 global financial crisis on housing market dynamics in an emerging economy like India using quarterly data (Q4 2008–2009 to Q1 2018–2019). The study explores the extent of linkages between housing prices, monetary policy and financial stability by explaining the nature of the shocks to the housing sector and the degree of impact of those shocks; the possibility of adverse feedback loop which is beyond the natural levels; and the usefulness of explicit and direct role of monetary policy for the housing market stability, which was the loudest demand immediately after the crisis.,The paper follows a three-step methodology: data transformations, a variable selection process “general-to-specific modelling” with the help of OxMetrics 6 Package, and vector autoregressive modelling with the help of EViews 10. F-test was used to describe the short-term relationships between the variables. Impulse response and variance decomposition were used to explain the type of relationship (negative or positive) and the period of the relationships, respectively.,The study finds that the housing sector is sensitive to the monetary policy shocks,",7.0,
a7207c2c11fa7636969f5434b856fb9acec52e10,10.3390/buildings15101744,Exploring the Joint Influence of Built Environment Factors on Urban Rail Transit Peak-Hour Ridership Using DeepSeek,2025.0,Buildings,Zhuorui Wang; Xiaoyu Zheng; Fanyun Meng; Kang Wang; Xin-Cheng Wu; Dexin Yu,"Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as poin",7.0,https://www.mdpi.com/2075-5309/15/10/1744/pdf?version=1747819237
6f2a88ca1b3d1e03f6e7e8abb8319de3a8cd3468,10.1186/s12889-024-19682-5,Measuring spatial inequalities in maternal and child mortalities in Pakistan: evidence from geographically weighted regression,2024.0,BMC Public Health,Farzana Sher Muhammad; Sharifah Muhairah Shahabudin; M. A. Talib,"In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extr",7.0,https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-024-19682-5
f084219ee5bd9d324e45e663034539ed51beff33,10.1371/journal.pone.0244665,How do city-specific factors affect migrant integration in China? A study based on a hierarchical linear model of migrants and cities,2021.0,PLoS ONE,Ru-Min Zheng; Lin Mei; Yanhua Guo; Shuo Zhen; Zhan-Hui Fu,"Background Previous studies indicate that migrant integration is associated with migrants’ characteristics as well as restrictions and opportunities in receiving cities. However, the effect of receiving cities and the relationship between migrants and receiving cities have not been fully explored due to the lack of large samples from cities. The objective of this study is to examine the effects of receiving cities alone and their regulating role in the interaction with individual characteristics. Methods Cross-city data on 154,044 Chinese domestic migrants above 15 years old in 289 cities from the 2017 China Migrants Dynamic Survey are used. Migrant integration is assessed by a four-dimensional model proposed by Esser, which is slightly adjusted according to the characteristics of Chinese migrants. A hierarchical linear model is used to measure the proportion of effects of city factors in migrant integration as well as the effects when city factors are considered alone and in interaction with individual factors. Results The individual-level and city-level factors are responsible for 69.81% and 30.19% of the effect on migrant integration, respectively. City political factors do not ",7.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0244665&type=printable
ae16455bdfeebc77c36b1f3edba9f89367649824,10.3390/land9110467,Assessing the Viability of Vacant Farmhouse Market in China: A Case Study in Sichuan,2020.0,Land,Qi Yin; Jin-Fu Hu; Zhan-Li Sun; Ding-De Xu; Gideon Ntim-Amo; Hong-Yan Tang,"Massive and rapid urbanization has led to population loss in rural areas, particularly in emerging and developing countries like China. As a result, houses in rural areas become vacant, and the house prices in cities, at the same time, skyrocket. While the research on the vacant farmhouses market (VFM) is a pressing issue for sustainable urbanization and has profound policy implications in China, few empirical studies have been conducted on analyzing the willingness of house owners and urban residents to participate in the VFM and any influencing factors—as there is no such operating market in China. To bridge the research gap, we first conducted a questionnaire-based survey on rural households and urban residents with a random sampling method in six cities in Sichuan Province, China. A total of 571 valid samples, including 284 rural households and 287 urban residents, were obtained. Based on these survey data, we then used logistic regression to estimate the influencing factors on the willingness of house owners and urban residents in renting in/out or selling/buying vacant farmhouses. The results showed that: (1) more than 60% of rural house owners and urban residents are willing",7.0,https://www.mdpi.com/2073-445X/9/11/467/pdf
5654b870d4405044056c4067b2b3fc923b7165be,10.1007/s11769-023-1341-5,"Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China",2023.0,Chinese Geographical Science,Yang Wang; Kang-Min Wu; Hong'ou Zhang; Yi Liu; Xiao-Li Yue,"Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positiv",7.0,http://egeoscien.neigae.ac.cn/EN/10.1007/s11769-023-1341-5
15d546c405ff9f384798bdab2f1e3abd5a9291a1,10.2139/ssrn.4381376,"Static and dynamic source identification of trace elements in river and soil environments under anthropogenic activities in the Haraz plain, Northern Iran.",2023.0,Science of the Total Environment,M. Mohammadi; M. Egli; A. Kavian; I. Lizaga,"Unsustainable human activities have disrupted the natural cycle of trace elements, causing the accumulation of chemical pollutants and making it challenging to determine their sources due to interwoven natural and human-induced processes. A novel approach was introduced for identifying the sources and for quantifying the contribution of trace elements discharge from rivers to soils. We integrated fingerprinting techniques, soil and sediment geochemical data, geographically weighted regression model (GWR) and soil quality indices. The FingerPro package and the state-of-the-art tracer selection techniques including the conservative index (CI) and consensus ranking (CR) were used to quantify the relative contribution of different upland sub-watersheds in trace element discharge soil. Our analysis revealed that off-site sources (upland watersheds) and in-site sources (land use) both play an important role in transferring trace elements to the Haraz plain (northern Iran). The unmixing model's results suggest that the Haraz sub-watersheds exhibit a higher contribution to trace elements transfer in the Haraz plain, and therefore, require greater attention in terms of implementing soil and",7.0,https://doi.org/10.2139/ssrn.4381376
a6d0804a3049a8287e419ee2478573a055829bdb,10.3390/land12010167,"Exploring the Spatial Discrete Heterogeneity of Housing Prices in Beijing, China, Based on Regionally Geographically Weighted Regression Affected by Education",2023.0,Land,Zengzheng Wang; Fuhao Zhang; Yangyang Zhao,"Spatial heterogeneity analysis of housing prices, in general, is crucial for maintaining high-quality economic development in China, especially in the post-COVID-19 pandemic context. Previous studies have attempted to explain the associated geographical evolution by studying the spatial non-stationary continuous heterogeneity; however, they ignored the spatial discrete heterogeneity caused by natural or policy factors, such as education, economy, and population. Therefore, in this study, we take Beijing as an example and consider educational factors in order to propose an improved local regression algorithm called the regionally geographically weighted regression affected by education (E-RGWR), which can effectively address spatial non-stationary discrete heterogeneity caused by education factors. Our empirical study indicates that the R2 and R2adj values of E-RGWR are 0.8644 and 0.8642, which are 10.98% and 11.01% higher than those of GWR, and 3.26% and 3.27% higher than those of RGWR, respectively. In addition, through an analysis of related variables, the quantitative impacts of greening rate, distance to market, distance to hospitals. and construction time on housing prices in ",7.0,https://www.mdpi.com/2073-445X/12/1/167/pdf?version=1672823096
33e69e85247e0e15edd217bc316de2f81c21fc0f,10.1155/2022/6249065,Real Estate Development Strategy Based on Artificial Intelligence and Big Data Industrial Policy Background,2022.0,Scientific Programming,Yu Liu,"In recent years, as one of the pillar industries of the national economy, the real estate industry has achieved unprecedented development. Since the 1990s, my country’s real estate industry has experienced three decades of rapid development. The average annual growth of commercial housing area is nearly 20%, and the average annual growth of housing prices in second-tier cities is 11.87%. However, in the rapid development, there are also many problems. For example, there are more and more phenomena such as unreasonable development, serious environmental pollution, and shortage of resources. At the same time, artificial intelligence has made great achievements in the development process in recent years, and it has continued to grow with technological progress and social demand. At present, China is in a transitional period of economic and social development, and the real estate industry is also facing huge challenges. In this context, research and development of traditional Chinese cities is very necessary and important. Therefore, how to effectively control and coordinate the real estate development behavior in the big data environment is one of the major problems that China is faci",7.0,https://downloads.hindawi.com/journals/sp/2022/6249065.pdf
1d955a19c330944ffad880a332a1cb5b55d7a37b,10.3390/land14050968,Analysis of the Evolution of Non-Agriculturization Arable Land Use Pattern and Its Driving Mechanisms,2025.0,Land,Ying Zhang; Qiang Wang; Yueming Hu; Wei Wang; Xiaoyun Mao,"Arable land is a crucial natural resource for human survival and development, which supports food production, ecological services, and material–energy cycling. It is not only an important production resource for agriculture but also a key guarantee for ensuring food security and sustainable agricultural development. Understanding the current utilization of arable land, exploring the spatial–temporal evolution characteristics, and analyzing the driving mechanisms behind its pattern changes are essential for the rational allocation and sustainable utilization of arable land resources. This study focuses on the utilization of arable land in Guangzhou from 2005 to 2018, employing methods such as statistical analysis and spatial econometrics to provide an in-depth analysis of the spatial–temporal distribution characteristics and driving mechanisms of arable land changes. The results show that from 2005 to 2018, the issue of the conversion of arable land to non-agricultural uses was quite severe in Guangzhou, with the primary form being the conversion of arable land into urban residential construction land. Kernel density analysis revealed that non-agriculturization in Guangzhou exhibite",7.0,https://www.mdpi.com/2073-445X/14/5/968/pdf?version=1746014712
a4db66f288023ce3007107b8f84f9367c0e90e47,10.1016/j.resourpol.2023.104197,"Whose Future, Whose Security?: Unconventional Oil and Gas Extraction and the Economic Vulnerability and Forced Participation of Small-scale Property Owners",2023.0,Resources policy,Stephanie A. Malin; Adam Mayer; Shawn Hazboun,"Since the ‘shale revolution’ of the late 2000s, unconventional oil and gas extraction (UOGE) has been hailed by many as a boon for local and regional economies, workers, and property owners. While energy social science has documented many realities that counter this narrative – natural resource dependent economies, ‘boom and bust’ cycles of the energy industry, and ‘resource curse’– there is less research examining economic impacts of UOGE for small-scale property owners. While some large-scale property owners lease their land, minerals, or water rights to oil and gas companies in exchange for royalties, other property owners are not able or do not wish to do so. Yet, nearby UOGE may negatively impact property values and thus threaten people’s sense of economic stability. Several legal mechanisms – forced pooling, split estate, and rule of capture – significantly restrict the rights of many property owners while privileging other property rights. While property ownership represents a privileged status, it is the largest investment many Americans will make and is relied upon for retirement planning, financial stability, and transfer of generational wealth. Yet, despite the importanc",7.0,https://doi.org/10.1016/j.resourpol.2023.104197
9c38392e828165a8c6124d118d697f3ddd8129d9,10.1109/access.2020.3019239,Siamese-Like Convolutional Neural Network for Fine-Grained Income Estimation of Developed Economies,2020.0,IEEE Access,Ruiqiao Bai; Jacqueline C. K. Lam; Victor O. K. Li,"Estimating the per-capita income and the household income at a fine-grained geographical scale is critical but challenging, even across the developed economies. In this article, a novel Siamese-like Convolutional Neural Network, integrating Ridge Regression and Gaussian Process Regression, has been developed for fine-grained estimation of income across different parts of New York City. Our model (the GP-Mixed-Siamese-like-Double-Ridge model) makes good use of the pairwise comparison of location-based house price information, daytime satellite image, street view and spatial location information as the inputs. Taking the per-capita income and the median household income in New York City as the ground truths, our model outperforms (R2 = 0.72–0.86 for five-fold validation) other state-of-the-art income estimation models and achieves good performance in cross-district and cross-scale validation. We also find that models which partially share our model architecture, including the Spatial-Information-GP and the Mixed-Siamese-like model, perform well under certain spatial granularity and data availability. Since such models rely on less data input types and simpler architectures, they can ",7.0,https://doi.org/10.1109/access.2020.3019239
d608c6872d6d6583cee90c7d20c1ef67d56d5745,10.1016/j.envint.2025.109576,Association of social and environmental exposures at the neighborhood level with child brain volume,2025.0,Environment International,Lina V. Dimitrov; G. Christensen; B. Ku; Benjamin B. Risk; Anke Hüls,"Background: Prior studies show that neighborhood disadvantage negatively impacts children’s cognitive function and brain volume. However, despite their co-occurrence, there is a lack of cohesive examination of the joint effects of environmental and social factors on brain volumes. Objectives: Using environmental mixture methods, we investigated the association between a child’s neighborhood environment and brain volume in regions relevant for cognitive and cognitive-affective function. Methods: We leveraged a sample of 8574 nine and ten-year-old children from the Adolescent Brain Cognitive Development study. Exposures included 22 social and 7 environmental exposures linked to participants’ baseline addresses. Neighborhood exposome profiles were represented using self-organizing maps. Baseline cortical and subcortical volumes in 16 regions were analyzed using linear mixed effects models with neighborhood exposome profile as a categorical exposure, adjusting for individual-level demographic variables and multiple testing. We used Weighted Quantile Sum (WQS) regression to examine the total mixture effect and the relative contribution of each individual exposure to the total mixture ef",7.0,https://doi.org/10.1016/j.envint.2025.109576
2e13b238a0fdd970e264ffbb13ae73f05e8c2182,10.3390/buildings15050708,Spatial Cluster Pattern and Influencing Factors of the Housing Market: An Empirical Study from the Chinese City of Shanghai,2025.0,Buildings,Yu-Hua Zhang; Boyana Buyuklieva,"Infrastructure and amenities have an evident effect on differentiated urban structures and house prices. However, few studies have taken into account the spatial heterogeneity of large-scale urban areas. Regarding this issue, the present study proposes a novel spatial framework to quantify the impacts of built environment factors on the housing market. We aim to answer: how does a specific factor impact house prices across different spatially autocorrelated neighbourhood clusters? The city of Shanghai, the economic centre of China, is examined through the transaction data from the China Real-estate Information Center (CRIC) are analysed. Firstly, spatially autocorrelation clusters were explored to identify high/low housing prices in concentrated areas in Shanghai. Secondly, using the development-scale house prices as the dependent variable, we employed ordinary least squares (OLS) linear regression and geographically weighted regression (GWR) models to examine the impact of built environment facilities on the house prices across these spatial autocorrelation regions and Shanghai more generally. The results suggest the following: (1) There are significant spatially autocorrelated cl",7.0,https://doi.org/10.3390/buildings15050708
f167d0cc2e990295c3a70461e3b6d2e5b6d505a1,10.3390/ijgi8050207,Analysis of Spatial Characteristics of Digital Signage in Beijing with Multi-Source Data,2019.0,ISPRS Int. J. Geo Inf.,Xun Zhang; Guang-Chi Ma; Li Jiang; Xiao-Hu Zhang; Ying Liu; Yuxue Wang; Cong-Hui Zhao,"Digital signage is an important medium for urban outdoor advertising. Understanding the spatial distribution characteristics and factors that influence the site of digital signage are conducive to the efficient, standardized, and sustainable development of digital signage. The outdoor commercial digital signage within the Sixth Ring Road in Beijing is taken as the research object, and social network check-ins, housing prices, traffic network centrality and the mount of commercial facilities are considered factors that influence digital signage. The spatial distribution characteristics of digital signage are studied by using point pattern analysis methods. Moreover, we use three spatial clustering algorithms to study the hierarchical spatial characteristics of digital signage and test the effectiveness of the results. In addition, the factors that influence the distribution of digital signage are analyzed by Spearman correlation analysis. The results indicate that (1) the digital signage in Beijing generally presents a relatively concentrated distribution with centrality and forms an obvious gathering area and the agglomeration centers are mainly concentrated in the core parts of th",7.0,https://www.mdpi.com/2220-9964/8/5/207/pdf?version=1557136958
570b61483ab1d03857407d0e0559e02cbe0520f3,10.14569/ijacsa.2021.0120628,What Drives Airbnb Customers’ Satisfaction in Amsterdam? A Sentiment Analysis,2021.0,,H. Madhi; Muna M. Alhammah,"The sharing economy is a new socio-economic system that allows individuals to rent out their personal belongings, such as their private car or a room in their home, for a short period. This study aims to investigate the attributes that impact customers’ satisfaction when using the sharing economy propriety rentals websites. Large data sets of Airbnb’s online reviews and listings in Amsterdam were analyzed using sentiment analysis, word clustering, ordinal logistic regression, and visualization techniques. Findings reveal that the polarity of Airbnb guests reviews in Amsterdam is significantly impacted by property price, value, cleanliness, rate, host communication, easiness of check-in, the accuracy of property description, and whether the host is super host or not. Surprisingly, the property neighborhood was not found to impact customers’ sentiment in Amsterdam. In addition, Airbnb guests in Amsterdam tend to positively express their experience satisfaction level mainly based on property exact location and host interaction followed with the facilities surrounding the property, property cleanliness, and room quality. On the other hand, negative online reviews tend to be mainly link",7.0,http://thesai.org/Downloads/Volume12No6/Paper_28-What_Drives_Airbnb_Customers_Satisfaction_in_Amsterdam.pdf
ffe19ef2e33a6e4e3685785056ada2c65ff2ae67,10.3390/ijerph18052711,Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data,2021.0,International Journal of Environmental Research and Public Health,Xin-Tao Liu; Ziwei Lin; Jianwei Huang; He Gao; Wen-Zhong Shi,"The measurement of medical service accessibility is typically based on driving or Euclidean distance. However, in most non-emergency cases, public transport is the travel mode used by the public to access medical services. Yet, there has been little evaluation of the public transport system-based inequality of medical service accessibility. This work uses massive real smart card data (SCD) and an improved potential model to estimate the public transport-based medical service accessibility in Beijing, China. These real SCD data are used to calculate travel costs in terms of time and distance, and medical service accessibility is estimated using an improved potential model. The spatiotemporal variations and patterns of medical service accessibility are explored, and the results show that it is unevenly spatiotemporally distributed across the study area. For example, medical service accessibility in urban areas is higher than that in suburban areas, accessibility during peak periods is higher than that during off-peak periods, and accessibility on weekends is generally higher than that on weekdays. To explore the association of medical service accessibility with socio-economic factors",7.0,https://www.mdpi.com/1660-4601/18/5/2711/pdf?version=1615428488
f6355f6d796f324b0aa183bad31df03418c1ff38,10.2478/remav-2014-0032,The Influence of Real Estate on Its Surroundings Based on the Example of the Manufaktura Complex in Lodz,2015.0,,Katarzyna Olbińska,"Abstract Real estate compared to other goods is distinguished by a high specificity. One of the signs of these specifics is the interdependence or correlation between property and its surroundings. On the one hand, the factor of closest neighborhood can largely shape the value of the property, while on the other hand, the function, use and development of real estate can strongly affect nearby property and the image of their entire neighborhood. The dynamics of the interdependence between the property and its environment reveal themselves most clearly in moments of transformation of real estate and its use. This study has a research character. The focus of this article is one of the aspects of interdependence - the impact of the property, its use and development on its closest surroundings. The aim of this article is to show the manifestations of different types of external effects generated by the property. Case study research was conducted on the housing market in Lodz for the purpose of the article. The study included the analysis of 3,444 transactions made during the period from July 2005 to December 2009 in the area of 27 precincts representing the proximal and distal environme",7.0,https://content.sciendo.com/downloadpdf/journals/remav/22/4/article-p5.pdf
4ce712df0f85f9ee756944167082b6851552d10c,10.1080/10406026.2021.1923926,Real Estate Damage Economics: The Impact of PFAS “Forever Chemicals” on Real Estate Valuation,2021.0,Environmental Claims Journal,Randall Bell; Michael Tachovsky,"Abstract The so-called “Forever Chemicals,” i.e. PFAS or PFCs, are emerging environmental contaminants. They refer to a family of carbon-fluorine chemicals that were initially developed in the commercial and industrial sectors around the time of WWII. As the term PFAS is a catch-all, there are many individual compounds, including C8, PFOA, PFOS, AFFF, PFNA, PFHxS, and GenX. Until the late 1990s/early 2000s, the health risks of PFAS compounds were largely unknown to the general public. But with the increasing ability to detect and monitor these compounds in humans and the environment, information is now more widely available. Health advisories are being developed at federal and state levels as to the adverse human health effects associated with PFAS are becoming clearer. As public awareness and the ability to detect PFAS compounds evolve, so must real estate valuations. Real estate valuation professionals are facing assignments that require an evaluation of the impact, if any, of PFAS on property values, in particular, properties with tainted household water. This article outlines the fundamental characteristics of PFAS compounds and sets forth valuation methodologies, such as mass ",7.0,https://www.tandfonline.com/doi/pdf/10.1080/10406026.2021.1923926?needAccess=true
3260b5763ddd41cd7a76b070f6f6217d78c21aa1,10.21660/2019.56.4601,HOLISTIC LANDSCAPE PLANNING`S VALUE FOR NATURAL DISASTER RECONSTRUCTION: WILLINGNESS TO PAY FOR NEW RESIDENCE IN DIFFERENT RECONSTRUCTION PLANNING APPROACHES,2019.0,International Journal of GEOMATE,Misato Uehara,"Practical evidence demonstrating the effectiveness of holistic landscape planning is limited because comprehensive decision-making is misunderstood as depriving individual sectors' authority and budgets. The author compared two municipalities’ difference of recovery speed and the quality of relocation housing; Shinchi town and Soma city, which are this project subjects, had contrasting planning processes from 2011 Japan Earthquake and Tsunami disaster. Shinch town has recovered by bottom-up the planning process and the author's natural disaster risk analysis, which based on 1969's McHarg's landscape planning principle. Whereas, Soma city has recovered by the top-down planning process, which was the same method as other municipalities affected by the tsunami. In this test, 263 students evaluated relocation houses’ prices without any information about differences of the planning process. The author analyzed subjects' attribute data that could potentially affect their evaluation: sex, the environment in childhood, etc. T-test and multiple regression analysis were performed to evaluate the effects of differences of the planning process for the value of the house. Welch's T-test found t",7.0,https://doi.org/10.21660/2019.56.4601
6392012560ad50ccdc2690be636fe30a7e9a6b49,10.1016/j.ufug.2021.127111,"Using phylogenetic diversity to explore the socioeconomic and ecological drivers of a tropical, coastal urban forest",2021.0,Urban Forestry & Urban Greening,Zhi-Xin Zhu; Francisco J Escobedo; L. Revell; T. Brandeis; Jun Xie; Hua-Feng Wang,"Abstract Relatively little is known about the dynamics of tropical urban forests, their phylogenetic diversity, as well as the socioeconomic and ecological factors that influence overall diversity across the urban landscape. However, permanent forest inventory and monitoring plot networks are increasingly being established across forests and cities of the world to monitor structural and functional attributes of urban forests, as well as ecosystem services. We analyzed a tropical, coastal urban forest in San Juan, Puerto Rico using metrics of phylogenetic diversity (PD), plant diversity, and available permanent plot data from an urban forest inventory and monitoring system. In total, we found that there were 152 species belonging to 119 genera and 44 families in San Juan. PD tended to be highest in forest remnants. We also explored correlations between socioeconomic factors and taxonomic diversity and found that plant species richness was correlated with population density and housing price. We found marginally significant relationships between housing price, population density, and several PD metrics. Our results further suggest mixed evidence of luxury and legacy effects, two fact",7.0,https://doi.org/10.1016/j.ufug.2021.127111
aca6cc30a34bc2afacc0bfbd51191916586e8698,10.1016/j.pmedr.2021.101612,Association between county-level sociodemographic characteristics and county-level differences in opioid dispensing,2021.0,Preventive medicine reports,Laura J. Cremer; Natasha L. Underwood; Amber B Robinson; G. Guy; Cherie R. Rooks-Peck,"Background While overall opioid prescribing has been decreasing in the United States, the rates of prescribing at the county level have been variable. Previous studies show that social determinants of health (the social and economic conditions in which we live) may play a role in opioid prescribing; however, researchers have not examined this relationship across US counties. This cross-sectional study seeks to determine whether county-level sociodemographic characteristics (e.g., economic, housing, social environment, healthcare environment, and population characteristics) are associated with county level differences in opioid dispensing. Methods Data from 2,881 counties in the United States from 2017 to 2018 were used for this study. Opioid dispensing was measured using morphine milligram equivalents (MME) per capita. Spatial error models were used to measure the association between county-level sociodemographic characteristics and MME per capita while adjusting for spatial correlation between neighboring counties. Results In the adjusted model, counties with a higher percentage of people below the poverty line, with less than a 4-year college degree, and without health insurance ",7.0,https://www.sciencedirect.com/science/article/pii/S221133552100303X/pdf
4cabdc330e092acabe670ddd4fa6c53bf921db64,10.31357/sljre.v19i1.6024,Critical Determinants of Residential Land Values in a Suburban Area: A Perception Analysis,2022.0,Journal of Real Estate Studies,A. M. D. Thiwanka; N. Wickramaarachchi,"
 
 
Residential land is a crucial factor for human beings in any country. Along with the rapid urbanization, the value of residential land is increasing gradually which sometimes the values are unreachable in some places. There is no evidence of research on what factors that are contributing to this escalation of land prices in Moratuwa, one of the suburban areas of the city of Colombo in Sri Lanka. Hence, the main intention of this research is to identify the important determinants of residential land values in the Moratuwa Municipal Council area. Data was collected from two groups, i.e., the residents and professionals who work with land values including three property valuers and a bank manager. An online questionnaire survey was used to collect data from residents selected through the purposive sampling method while semi structured interviews were conducted with professionals. The analysis proceeds with both quantitative and qualitative approaches. The results of the quantitative analysis used for residents’ views show that the factors of location, neighbourhood characteristics, accessibility, and topographic characteristics are significantly influence on land value. The resul",7.0,https://journals.sjp.ac.lk/index.php/SLJRE/article/download/6024/4457
163a1e2233d261dc83973863a535ff32bdbc3919,10.1515/remav-2017-0015,Changes On the Housing Market of the Downtown Area in Selected Polish Cities,2017.0,,I. Rącka; S. Palicki; M. Krajewska; K. Szopińska; O. Kempa,"Abstract Large Polish cities are currently dealing with an increasing significance of downtown areas, extending outside of the city centers (meaning the area directly surrounding the city square). The downtown alone seems to influence the fate of entire cities, facilitating their success or contributing to their failure. A good demographic, social and economic condition of a downtown, its positive image and the dynamic development of the part of the city perceived as the business and administration centre and a meeting place of residents and tourists, contribute to the image and potential of the whole city to a great extent. Changes in urban surroundings, the signs of which may be observed in spatial, aesthetic, architectural, urban-planning and socio-economic aspects, determine the functioning and condition of local real estate markets. Whether potential buyers consider the real estate attractive depends on the assessment of its significant features, of which transaction price is representative. The main research objective of the article is the identification, assessment and interpretation of differences in prices registered in the years 2009-2014 in downtown residential real esta",7.0,https://content.sciendo.com/downloadpdf/journals/remav/25/2/article-p79.pdf
b19a4b2e0a5444cc3454e406df917dda516db414,10.1016/j.identj.2024.03.009,Sociodemographic factors impacting the spatial distribution of private dental clinics in major cities of Peoples Republic of China,2024.0,International Dental Journal,Peng-Bo Liu; Xuyuan Zhang; Guoying Deng; Wei-Hua Guo,"Objectives Investigate the geographical distribution of private dental practices in major Chinese cities and analyze the variables influencing this distribution. Methods This study used Python to extract various types of Point of Interest (POI) data spanning from 2016 to 2022 from the AutoNavi map. A 1km*1km grid was constructed to establish the study sample. Additional spatial pattern data, including nighttime lighting, population, and air quality data, were integrated into this grid. Global Moran's I index was used to analyze the spatial autocorrelation. The spatial lag model was used to explore the influencing factors of private dental practice distribution. Results This study reveals a specific clustering pattern for private dental practices in major Chinese cities. The primary influencing factors include nighttime lights, population density, and housing prices, suggesting that dental practices are typically concentrated in highly developed regions with dense populations and high housing costs. Additionally, we discovered that patterns vary across different metropolises, with the most pronounced clustering patterns and substantial inequalities found in the most developed areas.",7.0,https://doi.org/10.1016/j.identj.2024.03.009
2f440e1c9a09e52924e786467c9e01bb95eec9af,10.1111/rego.70052,"Noisy Politics, Quiet Technocrats: Strategic Silence by Central Banks",2025.0,Regulation &amp; Governance,Benjamin Braun; Maximilian Düsterhöft,"In contrast to the “quiet” politics of the pre‐2008 period, macroeconomic policy has become “noisy”. This break raises a question: How do independent agencies designed for quiet politics react when a contentious public turns the volume up on them? Central banks provide an interesting case because while they are self‐professed adherents to communicative transparency, individual case studies have documented their use of strategic silence as a defense mechanism against politicization. This paper provides a quantitative test of the theory that when faced with public contention on core monetary policy issues, central banks are likely to opt for strategic silence. We focus on the most contested of central bank policies: large‐scale asset purchase programs or “quantitative easing” (QE). We examine four topics associated with particularly contested side effects of QE: house prices, exchange rates, corporate debt, and climate change. We hypothesize that an active QE program makes a central bank less likely to address these topics in public. We further expect that the strength—and, in the case of the exchange rate, the direction—of this effect varies depending on the precise composition of a",7.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/rego.70052
2e8599439d33c6ee491fe69e03ddfc1159fa0246,10.12776/qip.v21i1.781,The Complex Interactions between Cities and Nature,2017.0,,Dani Broitman; D. Czamanski; Marina Toger,"Purpose: Proximity to nature is highly valued by urbanites. They demonstrate higher willingness to pay for housing at locations near open and green spaces. But, nature in cities can generate negative externalities as well. The aim of this paper is to present the complex relationship between nature and cities and the possible negative influence of urban nature on property prices. Methodology/Approach: The data presented in this paper include open spaces, the presence of wild animals and residential property values in Haifa, Israel. These data were analyzed to uncover spatial regularities and basic statistical relationships. Findings: The results reveal the expected presence of dominant positive externalities related to proximity to open and green areas. However, in certain areas and under certain circumstances, the nuisances generated by the presence of wild animals in close proximity to housing are correlated with lower property prices. Research Limitation/implication: We demonstrate in this paper that that there is a complex relationship between nature and cities, albeit focusing our analysis on large mammals in cities only. Disentangling positive and negative externalities of urb",7.0,http://www.qip-journal.eu/index.php/QIP/article/download/781/758
7fe37a38138af56180f31def64e34e1f61d7de77,10.22214/ijraset.2022.44033,Prediction of House Prices Using Machine Learning,2022.0,International Journal for Research in Applied Science and Engineering Technology,S. Mysore,Abstract: Economy of the country is greatly driven by the prices of houses in that country. Both buyers and sellers depend on the pricing strategies. Ask an emptor to explain the factors they think are considered for pricing the house at that price and that they probably start with railways and end with various attributes. Over here it proves that more factors will be applied on the pricing strategies of the house. The aim of the project is to predict the house prices with various regression models. Nowadays Machine Learning is a booming technology. Data is the heart of Machine Learning. AI and Machine Learning holds the key position in the technological market. All industries are moving towards automation. So we have considered ML as a main predicting subject in our project and worked using it. These days everything fluctuates. Starting with crypto and various business models varies day by day which includes real estate as well so in this project house prediction depends on real estate data and ML techniques. Many people want to buy a good house within the budget. But the disadvantage is that the present system doesn’t calculate the house predictions so well and end up in loss of ,7.0,https://doi.org/10.22214/ijraset.2022.44033
832e6f7f1e3f0be1ae36c1f0b9adf7a071a15c46,10.3390/ijgi14100372,"Mobilities in the Heat: Identifying Travel-Related Urban Heat Exposure and Its Built Environment Drivers Using Remote Sensing and Mobility Data in Chengdu, China",2025.0,ISPRS Int. J. Geo Inf.,Yue Zhang; Xiaojiang Xia; Yang Zhang; Ling Jian,"Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure index (THEI) that combines ride-hailing trajectories and remote sensing data to capture dynamic human–environment thermal interactions. Using Chengdu, China, as a case study, the THEI is combined with local indicators of spatial association to outline high-exposure risk zones (HERZ). XGBoost with SHAP and partial dependence plot (PDP) methods is also applied to identify the nonlinear effects of built environment factors. Results showed the following: (1) distinct spatial clustering of high travel-related heat exposure in central urban districts and transit hubs; (2) city-wide exposure is primarily driven by transportation accessibility and urban form, such as intersection density and floor area ratio; (3) in contrast, HERZ are more strongly associated with demographic and socioeconomic factors, including population density, housing price and road density; and (4) vegetation, ",7.0,https://www.mdpi.com/2220-9964/14/10/372/pdf?version=1758723769
7f252e98cbfc765201a47857cc782cb733ea75f7,10.3390/ijgi14030116,The Effects of Employment Center Characteristics on Commuting Time: A Case Study of the Seoul Metropolitan Area,2025.0,ISPRS Int. J. Geo Inf.,Sangyeon Nam; Sungjo Hong,"The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income work",7.0,https://doi.org/10.3390/ijgi14030116
b08e10ef3468724d81e8819cbe6a7e12bb51240c,10.1016/j.jclepro.2024.143444,Understanding human mobility and trip demand through sparse trajectories of private e-bikes,2024.0,Journal of Cleaner Production,Pei-Xiao Wang; Hengcai Zhang; Shi-Fen Cheng; Feng Lu; Tong Zhang; Ze-Qiang Chen,"Understanding human mobility and trip demand through e-bike trajectories is crucial for urban planning, environmental enhancement, and sustainable development. However, existing studies predominantly focus on shared (e (cid:0) )bike trips, neglecting private e-bike trips. With the recent availability of sparse trajectory for private e-bikes, we established a novel analysis framework to reveal human mobility and trip demand in Wuhan, China. First, we propose a two-step method for extracting trip behavior from sparse trajectories of private e-bikes, involving the identification of staying areas and the generation of e-bike trips. Second, we establish a spatial random forest method to capture the nonlinear relationship between private e-bike trips and driving factors. Finally, we use the interpretable SHAP method to reveal the driving mechanisms of e-bike trips and explore the impact of various factors on these trips. The results indicate that (1) trip distances of private e-bikes follow a lognormal distribution, with an Adj. R-Square of 0.99, while trip times exhibit a Hill distribution, with an Adj. R-Square of 0.95; (2) Private e-bike trips are not commonly employed to address the ",7.0,
a8361723d27a163108c36779a49323e1a1ffb9ff,10.1111/tgis.13273,Developing GeoAI Integrated Mass Valuation Model Based on LADM Valuation Information Great Britain Country Profile,2024.0,Trans. GIS,M. Mete,"Access to information pertaining to land facilitates strategic planning for individuals, businesses, and governmental entities and allows them to anticipate and address contemporary challenges such as climate change, housing requirements, and economic prosperity. Improvements in information technologies contribute to data creation, analysis, and dissemination processes that enable gaining knowledge about land‐related events. Greater use of location data within property technologies gives rise to an efficient and sustainable land administration system. GeoAI can contribute greatly to the solution of spatial problems by extracting information from complex data through geospatial intelligence. Property valuation requires a spatially explicit model to evaluate locational factors with spatial analyses. In this study, a Land Administration Domain Model (LADM)‐based mass valuation model is developed for Great Britain by integrating GeoAI techniques. First, the LADM Valuation Information package is matched with the country's current organizational structure and it is extended according to the needs to create a country profile. After developing the conceptual model, a database schema is cre",7.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/tgis.13273
451ce51ac09496734c8817fde1708c834682f4e3,10.3390/nu13020479,Inedible Food Waste Linked to Diet Quality and Food Spending in the Seattle Obesity Study SOS III,2021.0,Nutrients,Shilpi Gupta; Chelsea M. Rose; James H. Buszkiewicz; Jennifer J. Otten; Marie L. Spiker; A. Drewnowski,"Americans waste about a pound of food per day. Some of this is represented by inedible food waste at the household level. Our objective was to estimate inedible food waste in relation to diet quality and participant socio-economic status (SES). Seattle Obesity Study III participants (n = 747) completed the Fred Hutch Food Frequency Questionnaire (FFQ) and socio-demographic and food expenditure surveys. Education and geo-coded tax-parcel residential property values were measures of SES. Inedible food waste was calculated from diet records. Retail prices of FFQ component foods (n = 378) were used to estimate individual-level diet costs. The NOVA classification was used to identify ultra-processed foods. Multivariable linear regressions tested associations between inedible food waste, SES, food spending, Nutrient Rich Food (NRF9.3) and Healthy Eating Index (HEI-2015) scores. Inedible food waste was estimated at 78.7 g/d, mostly from unprocessed vegetables (32.8 g), fruit (30.5 g) and meat, poultry, and fish (15.4 g). Greater inedible food waste was associated with higher HEI-2015 and NRF9.3 scores, higher food expenditures and lower percent energy from ultra-processed foods. In multiv",7.0,https://www.mdpi.com/2072-6643/13/2/479/pdf?version=1612334146
aef02816fdd297caece399a75ab9eb31bd024ec6,10.1109/access.2020.2987717,A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network,2020.0,IEEE Access,Xun Zhang; Xiaolan Xie; Yuxue Wang; Xiao-Hu Zhang; Dong Jiang; Chongchong Yu; Yike Liang,"Digital signage is an important outdoor advertising medium in cities. However, advertising on digital signage often lacks pertinence. Thus, it is important to introduce an accurate digital signage audience classification method to facilitate targeted advertising. In this study, a multi-label classification model based on a backpropagation (BP) neural network and the Huff model, referred to as the Huff-BP model, is proposed to investigate digital signage audience classification. A case study is performed on outdoor digital signage within the 6th Ring Road in Beijing, China, and economic census, population census, average housing price, social media check-in and the centrality of traffic networks as research data. The data are divided into $100\times100$ -1, $000\times1$ ,000 m normal grids. Multi-label classification modelling factors for various grid scales are constructed. The BP neural network classification algorithm is improved to solve the multi-label classification problem. In addition, an improved Huff model is used to calculate the digital signage influence values between each grid cell and integrated into the improved BP neural network to classify modelling factors at vari",7.0,https://doi.org/10.1109/access.2020.2987717
810a9cf68bb6665ec1b80044e434a516c1f4ffdb,10.1016/j.jenvman.2025.125828,Unraveling the spillover effects of socio-governance on circular transition: Does contract monitoring sand or grease the wheels of circular economy?,2025.0,Journal of Environmental Management,Vasilis Nikou,"Governments worldwide have prioritized addressing global climate change by integrating the circular economy (CE) principles into public procurement practices. The driving force behind this study is to investigate the spillover effects of socio-governance factors on the CE transition, focusing on the role of contract monitoring. Panel data encompassing 16 EU countries that have made limited progress in meeting their greenhouse gas emission targets from 2010 to 2019 are utilized for the analysis. To estimate the spatial spillover effects, various models including spatial Durbin models, spatial lag regression models, spatial error models with spatial and time fixed-effects, spatial autocorrelation and mixed-effects ML and REML regression models are employed. Findings reveal that robust contract monitoring plays a significant role in enhancing CE, emphasizing that transparent public procurement and accountable decision-making are key drivers of the circular transition. Legal frameworks that protect property rights and promote regulatory quality further facilitate CE adoption, with positive spatial spillover effects suggesting that well-governed countries indirectly encourage CE impleme",7.0,
7d720d03f070a188f835742813c63047cf0c3f9c,10.3390/app131910732,Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning,2023.0,Applied Sciences,Juryon Paik; Seungyun Baek; Jun-Wan Kim; Kwangho Ko,"The South Korean residential real estate market is influenced by both the traditional dynamics of demand and supply and external factors such as housing policies and macroeconomic conditions. Considering the proportion of housing assets in individual wealth, market fluctuations can have significant implications. While previous studies have utilized variables such as GDP growth rate, patent issuance, and birth rate, and employed models such as LSTM and ARIMA for housing price predictions, many have overlooked the influence of local factors. In particular, there has been insufficient investigation into the impact of subway stations and living social overhead capital facilities on housing prices, especially in metropolitan areas. This study seeks to bridge this gap by analyzing the usage trends of subway stations, evaluating the impact of living social overhead capital facilities on housing values, and deriving the optimal machine learning model for price predictions near subway stations. We compared and analyzed a total of eight machine learning regression models, including Linear Regression, Decision Tree, Random Forest, LightGBM, Ridge, Lasso, Elastic Net, and XGBoost, all of which",7.0,https://www.mdpi.com/2076-3417/13/19/10732/pdf?version=1695799534
b3fdfbd585aa1f06e645bea515ac4e15a1e0148b,10.1371/journal.pone.0265675,Spatial evolution and influencing factors of religious places from a socio-spatial perspective: An empirical analysis of Christianity in China,2022.0,PLoS ONE,Yujuan Chen; Ning Lin; Liang Ding; Jianping Qu; Qiang Zhou,"The mismatch between the supply and demand of religious places has resulted from a lack of management, population mobility, and urban socio-spatial differentiation. Furthermore, the identity of religious groups and the localized characteristics of their behavioral practices continue to dominate the evolution of religious places. Therefore, this study analyzed the spatio-temporal patterns of Christian activity places, and further studied the characteristics and mechanisms of various influencing factors on the spatial distribution of places using a geographically weighted regression model from a socio-spatial perspective. The following results were obtained: (1) Christian activity places form a distribution pattern of circling aggregation within the core area and polycentric dispersion outside the core area. There is a certain coupling relationship between spatio-temporal patterns of places and marketization in Yiwu City. (2) The spatial differentiation pattern of influencing factors has an obvious circle-layer characteristic. The regression coefficients of population mobility, age structure, public services, and jobs-housing space are larger, and social structure and spatial resourc",7.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0265675&type=printable
8285c64ff4f25bcb865858df6cebed7233fedb55,10.3390/su11247232,Quantitative Estimation and Spatiotemporal Characteristic Analysis of Price Deviation in China's Housing Market,2019.0,,Xiao-Ping Zhou; Zhen Qin; Ying-Jie Zhang; Linying Zhao; Yanjiao Song,"Differences in housing prices and rental prices across cities or regions and the relationship between prices and socioeconomic fundamentals are frequent research foci in urban and real estate economics, but the existing studies on China's housing market rarely consider both housing price and rent. This study provides a framework for a quantitative analysis of a country's housing market from the perspective of supply and demand, and takes China's housing market as a case study. The current study first explores the key factors that affect housing prices and rental prices using data from 202 cities and collected from 2011 to 2014. Then, theoretical values of housing prices and rental prices in each city are estimated, and the spatiotemporal characteristics of deviation of housing prices and rental prices are analysed. The empirical findings of the current study mainly reveal the following three points. First, the determinants of housing prices and rental prices have similarities. Second, the effect of above factors shows obvious spatial heterogeneity. The coefficients of the variables are different between coastal and inland regions, indicating different demand and supply elasticities",7.0,https://doi.org/10.3390/su11247232
257b53b8439c8135e8306325d592117f951f99af,10.30534/ijatcse/2020/235932020,Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes,2020.0,International Journal of Advanced Trends in Computer Science and Engineering,Waleed T. Al-Sit,"Country’s economic status can derived from many complex and branched indicators, one of which is property prices estimation. Working on such indicator changed the state of literature from many perspectives and corners. Whilst the scarcity of such works imposes a need for it, and demonstrates an unutilized aspect of the economy that requires little resources to create some business and academic opportunities. In this work, efforts evolved to address the problem of estimating properties prices accurately, in specific apartment’s prices among The Amman City, The Capital of The Hashemite Kingdom of Jordan. Leading to shed the lights on employing data science different techniques namely data processing, analysis and predictive modeling for adopting and estimating the apartment’s prices based on advertisement data published through the web and its extracted location geocodes. In addition, the work evaluates the final analysis reported results based on selected evaluation measures, and compare them with other five similar works on such problem conducted in other countries. Trying to aim to enrich the literature with valuable insights gained using Machine Learning and Data Mining different",7.0,https://doi.org/10.30534/ijatcse/2020/235932020
6653073f36f91f417483a00f1e9f7784f5139102,10.3390/su12166304,Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis,2020.0,Sustainability,Antonio Nesticò; Marianna La Marca,"It is well known that production activities are often the cause of ecosystem disservices. Such disservices can have serious effects on urban real estate values. But how much is the contraction that the market values of housing suffer due to the polluting emissions produced by a medium-sized foundry? And how large is the urban area within which buildings are depreciated? With this research we intend to give an answer. To this aim, with specific regard to urban apartments free from contractual constraints, the use of multiple regression analysis makes it possible to obtain a function that explains the real estate value through multiple variables, one of which is representative of the ecosystem disservice. The study reveals that the urban area that suffers from the negative effects of polluting industrial activities on property prices can be extensive. On the other hand, the contractions of real estate values can even reach 43%. These results, for the first time expressed in quantitative terms, must direct towards urban planning interventions, and more generally of economic policy, aimed at minimizing the environmental impacts of production activities. This is not only for the essenti",7.0,https://www.mdpi.com/2071-1050/12/16/6304/pdf?version=1596711127
e7a547f64b1e27fd8bc5306c7b0f67a94cad19b4,10.3390/rs15030863,Machine Learning of Usable Area of Gable-Roof Residential Buildings Based on Topographic Data,2023.0,Remote Sensing,L. Dawid; Kacper Cybiński; Żanna Stręk,"In real estate appraisal, especially of residential buildings, one of the primary evaluation parameters is the property’s usable area. When determining the property price, Polish appraisers use data from comparable transactions included in the Real Estate Price Register (REPR), which is highly incomplete, especially regarding properties’ usable areas. This incompleteness renders the identification of comparable transactions challenging and may lead to incorrect prediction of the property price. We address this challenge by applying machine learning methods to estimate the usable area of buildings with gable roofs based only on their topographic data, which is widely available in Poland in the Database of Topographic Objects (BDOT10k) of Light Detection and Ranging (LiDAR) origin. We show that three features are enough to make accurate predictions of the usable area: the covered area, the building’s height, and the number of stories optionally. A neural network trained on buildings from architectural bureaus reached a 4% median percentage error on the same source and 15% on the real buildings from the city of Koszalin, Poland. Therefore, the proposed method can be applied by apprais",7.0,https://www.mdpi.com/2072-4292/15/3/863/pdf?version=1675840764
0e8ea76cf6036d5cb5aee397f4bffefcfdbbcbd6,10.3390/realestate1010005,Impact of Green Features on Rental Value of Residential Properties: Evidence from South Africa,2024.0,Real Estate,T. B. Odubiyi; R. Abidoye; C. Aigbavboa; W. Thwala; A. S. Ademiloye; O. Oshodi,"In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and impleme",7.0,https://www.mdpi.com/2813-8090/1/1/5/pdf?version=1710906638
b1203923f561909463d777893f5a8b9962437278,10.54691/bcpbm.v32i.2881,Research on the House Price Forecast Based on machine learning algorithm,2022.0,BCP Business &amp; Management,W.-S. Weng,"House price experiences some fluctuations every year, due to some potential factors such as location, area, facilities and so on. Housing price prediction is a significant topic of real estate, and it is beneficial for buyers to make strategy decisions about house dealing. There are many research on house price forecast, yet the current research cannot comprehensively compare and analyze the popular house price prediction approach. Constructing a model begins with pre-processing data to fill null values or remove data outliers and the categorical attribute can be shifted into required attributes by using one hot encoder methodology. This paper used the following five algorithms decision tree, random forest regression, Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and extreme gradient boosting (XGBoost) this paper utilized to predict house prices and compared according to the root mean squared error. This paper found GBDT and XGBoost have more accurate prediction results compared with other algorithms. Besides, this paper found which features most affect the price of a house. In real-world applications, machine learning based housing price prediction models a",7.0,https://bcpublication.org/index.php/BM/article/download/2881/2847
f3bf12c7c903d2eae7ba97f32b506850ec57d136,10.1365/s41056-022-00065-z,Adverse selection in iBuyer business models—don’t buy lemons!,2022.0,Zeitschrift für Immobilienökonomie,Eirik Helgaker; A. Oust; A. J. Pollestad,"The rise of instant buyer (iBuyer) businesses in the past years has made automated valuation models (AVMs) an important part of the property market. Although iBuyer services are in demand, large actors within the segment have reported dissatisfying profits over time. The business model is subject to adverse selection as homeowners based on their superior knowledge of their home are more likely to accept overpriced bids than underpriced bids, making the iBuyer purchase more overpriced dwellings. In this paper, we use a dataset consisting of 84,905 apartment transactions from Oslo, the Norwegian capital. We use 80% of the dataset to train three different AVMs similar to those used by iBuyers. Next, we construct some simple purchasing rules from the predictive accuracies found in the training dataset. Finally, taking the remaining 20% of the data in a test dataset, we introduce an adverse selection indicator based on accepted probability distributions and calculate the average expected resale profits per apartment for a hypothetical iBuyer. We find that adverse selection has a large negative impact on average profits for the hypothetical iBuyer. Furthermore, the simple purchasing rule",7.0,https://link.springer.com/content/pdf/10.1365/s41056-022-00065-z.pdf
ab6cea631fa8dbe29e917b2c78ef50959bf30eff,10.3390/land12010125,"Assessing the Impact of Selected Attributes on Dwelling Prices Using Ordinary Least Squares Regression and Geographically Weighted Regression: A Case Study in Poznań, Poland",2022.0,Land,Cyprian Chwiałkowski; A. Zydroń; D. Kayzer,"The price of dwellings is determined by a number of attributes among which location factors are usually the most important. Comprehensive analyses of the real estate market should take into account a broad spectrum of attributes including economic factors, physical, neighborhood and environment characteristics. The primary objective of the study was to answer the question of what determinants affect transaction prices within the housing market in Poznań. The analysis was performed on the basis of source data obtained from the Board of Geodesy and Urban Cadastre GEOPOZ in Poznań. In our study, we used two research regression methods: ordinary least squares and geographically weighted regression. The estimated models made it possible to formulate specific conclusions related to the identification of local determinants of housing prices in the Poznań housing market. The results of the study confirmed that the use of the proposed techniques makes it possible to identify attributes relevant to the local market, and, moreover, the use of spatial analysis leads to an increase in the quality of the description of the characteristics of the analyzed phenomenon. Finally, the results obtained",7.0,https://www.mdpi.com/2073-445X/12/1/125/pdf?version=1673517967
74dcccd85bf2f164d137e196c56da9391da5222d,10.1088/1757-899x/640/1/012001,Impact of Security on Rental Price of Residential Properties: Evidence from South Africa,2019.0,IOP Conference Series: Materials Science and Engineering,T. B. Odubiyi; O. Oguntona; O. Oshodi; C. Aigbavboa; W. Thwala,"The property market plays a vital role in the economy through the provision of constructed space for productive activities and employment opportunities. Evidence gleaned from the literature suggests that the level of security within an area affects property prices. In the current study, the distance between a property and the police station was used as a proxy variable for measuring the perceived level of security. The data on rental prices of residential properties and its attributes were retrieved from a reliable property source (www.property24.com). A Neural network model was used for evaluating the impact of the presence of a police station on rental prices of residential properties within Cape Town, South Africa. Experimental results showed that the developed model is 77.27% accurate when used to predict the rental prices of residential properties. Floor area, number of bathroom, number of bedroom and proximity of a police station have the most significant impact on the rental price of residential properties. Greater efforts are needed to provide insights into the effect of sustainability on rental prices of residential properties. This information would serve a justification ",7.0,https://iopscience.iop.org/article/10.1088/1757-899X/640/1/012001/pdf
ff5814bff8b84308a92d9c3a78788cafc8b5a77a,10.48550/arxiv.2601.20819,Demystifying Prediction Powered Inference,2026.0,Harvard data science review,Yi-Lin Song; Dan M. Kluger; Harsh Parikh; Tian Gu,"Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science. However, treating predictions as ground truth introduces bias while ignoring them wastes valuable information. Prediction-Powered Inference (PPI) offers a principled framework that leverages predictions from large unlabeled datasets to improve statistical efficiency while maintaining valid inference through explicit bias correction using a smaller labeled subset. Despite its potential, the growing PPI variants and the subtle distinctions between them have made it challenging for practitioners to determine when and how to apply these methods responsibly. This paper demystifies PPI by synthesizing its theoretical foundations, methodological extensions, connections to existing statistics literature, and diagnostic tools into a unified practical workflow. Using the MOSAIKS housing price data, we show that PPI variants produce tighter confidence intervals than complete-case analysis, but that double-dipping, i.e. reusing training data for inference, leads to anti-conservative confidence intervals and b",7.0,
ccb4adf578a1398e942219b3e38f90c3ce52d1bf,10.1093/jeg/lbad035,Localized effects of confiscated and re-allocated real estate mafia assets,2023.0,Journal of Economic Geography,Filippo Boeri; Marco Di Cataldo; Elisabetta Pietrostefani,"
 Asset confiscation regimes are present throughout Europe. The Italian State implements a policy stipulating the confiscation of real estate assets from individuals convicted of mafia-related crimes and the re-allocation of these assets to new uses. The policy of confiscation (confisca) and re-allocation (destinazione) is meant to act as both an anti-mafia measure and a way to compensate local communities by converting real estate assets into public amenities. We evaluate the effects of this programme on local areas by estimating its impact on property values in the proximity of confiscations and re-allocations. The findings reveal a negative effect of confiscations and a positive effect of re-allocations on housing prices, mainly visible in mafia strongholds. Both of these trends can be observed in the time periods that directly follow instances of confiscation and re-allocation and appear to be highly localized. A drop in organized crime activity in the streets where re-allocations have occurred could account for some of the gain in property values caused by re-allocations. These findings have implications for the effectiveness of anti-mafia initiatives aimed at improving the qu",7.0,https://iris.unive.it/bitstream/10278/5045981/1/Boeri_DiCataldo_Pietrostefani_JoEG.pdf
73f6ad0cfe50fd4f9938d57900a75a91bb9a4b9e,10.28991/cej-sp2023-09-04,Forecasting the Real Estate Housing Prices Using a Novel Deep Learning Machine Model,2023.0,Civil Engineering Journal,H. H. Mohamed; A. Ibrahim; Omar A. Hagras,"There is an urgent need to forecast real estate unit prices because the average price of residential real estate is always fluctuating. This paper provides a real estate price prediction model based on supervised regression deep learning with 3 hidden layers, a Relu activation function, 100 neurons, and a Root Mean Square Propagation optimizer (RMS Prop). The model was developed using actual data collected from 28 Egyptian cities between 2014 and 2022. The model can forecast the price of a real estate unit based on 27 different variables. The model is created in two stages: adjusting the parameters to obtain the best ones using a sensitivity k-fold technique, then optimizing the result. 85 percent of the real estate unit data gathered was used in training and developing the model, while the other 15 percent was used in validating and testing. By using a dropout regularization technique of 0.60 on the model layers, the final developed model had a maximum error of 10.58%. After validation, the model had a maximum error of about 9.50%. A graphical user interface (GUI) tool is developed to make use of the final predictive model, which is very simple for real estate developers and decis",7.0,https://www.civilejournal.org/index.php/cej/article/download/3947/pdf
5f89970555b31e1798b47a89ec00e1c3e366d091,10.21834/e-bpj.v2i5.702,"Characteristic Qualities Impacting Landed House Prices: Better homes, better livelihood",2017.0,,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; Delphi ISSN: 2398-4287 © 2017. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning",7.0,https://doi.org/10.21834/e-bpj.v2i5.702
b08ca69d7053a8e9c90f5a90e628061e10da0923,10.1177/23998083211064625,The economics of architectural aesthetics: Identifying price effect of urban ambiences by different house cohorts,2022.0,Environment and Planning B Urban Analytics and City Science,Ka-Shing Cheung; Chung Yim Edward Yiu,"Although interest in sensory atmospheres and urban ambiences has been growing in urban studies, little is known about the economics of such ambiences. In architecture research, urban atmospheres or ambiences have been studied qualitatively through surveys and interviews. So far, the value of ambience has been focused and quantified only for heritage buildings. However, non-heritage buildings and open spaces also shape urban ambiences, either positively or negatively. This paper seeks to empirically disentangle cohort ambience effects on house prices based on the proportion of various house cohorts to the total number of houses within either a 0.5 km or a 1.0 km radial circle. Based on more than 56,000 housing transactions from 2010 to 2019 in Auckland, New Zealand, the results confirm that houses in a neighbourhood with historic ambience have a premium of more than 20%, ceteris paribus. This finding opens up a new research agenda on the economics of architectural aesthetics and suggests that there should be further studies of the price effects of various types of urban ambiences. The results may also have important implications for architects, urban planners and government official",7.0,https://journals.sagepub.com/doi/pdf/10.1177/23998083211064625
edd785d2ee888ca78237a0107d2b417fca3fc29c,10.1140/epjds/s13688-023-00424-3,Does noise affect housing prices? A case study in the urban area of Thessaloniki,2023.0,EPJ Data Science,Georgios Kamtziridis; D. Vrakas; Grigorios Tsoumakas,"Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of th",7.0,https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-023-00424-3
9b685ab6730eaa424ec676aeae43988a462d2660,10.23919/icif.2018.8455785,Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning,2018.0,Fusion,Danil Kuzin; Le Yang; O. Isupova; L. Mihaylova,"Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.",7.0,https://arxiv.org/pdf/1807.03369
838ddf4c4c072730d1242528bba13902e690c986,10.3390/ijgi11050283,Identifying Multiple Scales of Spatial Heterogeneity in Housing Prices Based on Eigenvector Spatial Filtering Approaches,2022.0,ISPRS Int. J. Geo Inf.,Peng Zhan; R. Inoue,"Interest in studying the urban real estate market, especially in investigating the relationship between house prices and related housing characteristics, is rapidly growing. However, this increasing attention is handicapped by a limited consideration of the multi-scale spatial heterogeneity in these relationships. This study uses the rental price data of 72,466 apartments in the Tokyo metropolitan area to examine spatial heterogeneity in the real estate market at multiple spatial scales. Within the framework of spatially varying coefficient (SVC) modeling, we utilized a random effect eigenvector spatial filtering-based SVC (RE-ESF-SVC) model, an approach not previously employed in real estate studies, and compared it with the traditional ESF-SVC model, which has no random effects. Our results show that: (1) except for one housing characteristic that impacts prices consistently throughout the Tokyo metropolitan area, relationships between other characteristics and prices vary from local to global spatial scales; (2) because of the utilization of random effects, RE-ESF-SVC has the unique advantage of making estimations flexibly while maintaining a high performance.",7.0,https://www.mdpi.com/2220-9964/11/5/283/pdf?version=1651290075
602e1fb4b1e820fd13056357b14a38759c9354ab,10.5198/jtlu.2021.1622,"Metro station inauguration, housing prices, and transportation accessibility: Tehran case study",2021.0,Journal of Transport and Land Use,Yekta Yazdanifard; Hosein Joshaghani; Masoud Talebian,"The opening of a new metro station, as a mode of the transportation corridor, potentially could have different effects on housing prices. We have investigated its effect on the value of residential properties around those stations, using data from large expansions of the metro network in Tehran, Iran. In the period of our study (April 2010 to December 2018), forty-five metro stations were inaugurated in Tehran. We use a difference-in-difference regression method to identify the causal effect of interest, where adjacent properties are used as the treatment group and similar but distant properties as the control group. The results indicate that, on average, the adjacent properties are affected by a 3.7 percent increase in price relative to distant properties. We also extend our study by categorizing new metro stations according to the extent of ex-ante access to other modes of public transportation such as bus rapid transit (BRT). We find 2 to 11 percent positive effect of new metro stations in regions with lower public transport, while in regions with ex-ante extensive public transportation system, we find less than 2 percent positive effect.",7.0,https://www.jtlu.org/index.php/jtlu/article/download/1622/1560
4ee3c7717db58b1ec87b34f06f913235e6cbae72,10.1007/s10584-023-03602-4,"Sea level rise risk interactions with coastal property values: a case study of O‘ahu, Hawai‘i",2023.0,Climatic Change,N. Tarui; Seth Urbanski; Q. Lam; Makena Coffman; C. Newfield,"Climate change-induced sea level rise (SLR) will affect a range of coastal assets and prompt difficult decisions about coastal land use across the world. Several recent studies find that current and projected SLR is associated with relatively lower property values. We contribute to this growing body of research with a case study of O‘ahu, Hawai‘i, which is famed for its beaches as well as valuable coastal real estate. We leverage a dataset that unpacks multiple types of SLR exposure and coastal parcel attributes. We apply property transaction data for the island of O‘ahu through 2019 to investigate the effect of current and expected SLR exposure on residential property prices. We find that exposed properties have already experienced declines in transaction prices, at 9 to 14%, attributed to expectations of exposure to chronic inundation (as opposed to seasonal flooding). The price declines are mainly for multi-dwelling homes as opposed to single family homes. The market response of residential properties to SLR has important implications for coastal management strategies, in particular the viability and timing of programs for retreat.",7.0,https://link.springer.com/content/pdf/10.1007/s10584-023-03602-4.pdf
a9a8dddc24209da2e0314d74cc9ecd2229b976ee,10.55606/jcsrpolitama.v1i6.3038,Analisis Prediksi Harga Rumah di Bandung Menggunakan Regresi Linear Berganda,2023.0,Journal of Creative Student Research,Rafif Nauval Tuah Siregar; Vijay Sitorus; Willy Pramudia Ananta,"This research aims to develop a model for estimating house prices in the Bandung area using the Multiple Linear Regression approach. House prices play a significant role in the decision-making process for purchases. The ability of this model to predict house prices with high accuracy provides significant benefits for potential buyers, sellers, and various stakeholders in the housing industry. Data on house prices and potential variables such as land area, building area, number of bedrooms, nearby facilities, and geographical location were collected for analysis. The use of Multiple Linear Regression allows for a deeper understanding of the relationships between these variables and the value of the house. The analysis results show a strong correlation between these variables and house prices in Bandung. The developed Multiple Linear Regression model can provide satisfactory predictions of house prices. This model can be used as a tool for both homebuyers and sellers to determine fair prices and assist property developers in identifying key factors influencing house prices in the Bandung region.",7.0,https://ejurnal.politeknikpratama.ac.id/index.php/jcsr/article/download/3038/2873
e54758e50de8667d0c6e558ef10bf6589e77107e,10.5194/isprs-annals-iv-4-w9-79-2019,THE IMPLEMENTATION OF BIG DATA ANALYSIS IN REGULATING ONLINE SHORT-TERM RENTAL BUSINESS: A CASE OF AIRBNB IN BEIJING,2019.0,"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",J. Li; Filip Biljecki,"Abstract. With the fast expansion and controversial impacts of short-term rental platforms such as Airbnb, many cities have called for regulating this new business model. This research aims to establish an approach to understand the impact of Airbnb (and similar services) through big data analysis and provide insights potentially useful for its regulation. The paper reveals how Airbnb is influencing Beijing’s neighbourhood housing prices through machine learning and GIS. Machine learning models are developed to analyse the relationship between Airbnb activities in a neighbourhood and prevailing housing prices. The model of the best fit is then used to analyse the neighbourhood price sensitivity in view of increasing Airbnb activities. The results show that the sensitivity is variable: there are neighbourhoods that are likely to be more price sensitive to Airbnb activities, but also neighbourhoods that are likely to be price robust. Finally, the paper gives policy recommendations for regulating short-term rental businesses based on neighbourhood’s price sensitivity.
",7.0,https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W9/79/2019/isprs-annals-IV-4-W9-79-2019.pdf
79d980c86de9e3e0a9329f59c12e7828bb1c431a,10.18267/j.polek.733,Rovnovážnost cen nemovitostí v České republice,2010.0,,Michal Hlaváček; Luboš Komárek,"This article, based on an empirical analysis, discusses factors affecting property prices and tries to identify periods of property price overvaluation in the Czech Republic. To achieve this, the article uses both relatively simple approach using ratios related to the house prices (e.g. price-to-rent or price-to-income indicators) as well as international comparison and more advanced econometrical approach. Within this econometrical approach we apply both time series analysis for the Czech Republic as a whole and panel regression for the Czech regions. This analysis identifies overvalued property prices in 2002-2003 and 2007-2008. In 2007-2008, however, the rise in property prices was largely explainable by fundamentals, meaning that the price overvaluation in this period was considerably smaller than that in the first one. From the regional perspective, there is a higher degree of overvaluation in regions with higher property prices. The exception is Prague, which seems to be a ""specific"" region - partly due to the properties of the estimation technique.",7.0,http://polek.vse.cz/doi/10.18267/j.polek.733.pdf
6235fbc7f5bb1d0e0991db299fc456133b53ee95,10.1108/ecam-10-2020-0850,Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm,2021.0,,Changro Lee; K. K. Park,"PurposeIt is important to forecast local trading volumes as well as global trading volumes because the real estate market is always characterized as a localized market. The house trading volume at the local level is forecast through appropriate models to enhance the predictive accuracy.Design/methodology/approachFour representative housing submarkets in South Korea are selected, and their trading volumes are forecast. A well-established time-series model and a deep learning algorithm are employed: the autoregressive integrated moving average (ARIMA) model and the recurrent neural network (RNN), respectively. The trading volumes in adjacent areas are utilized as covariates, and an ensemble prediction is applied additionally to improve the model performance.FindingsThe results indicate no significant difference in prediction performance between the ARIMA model and the RNN, which can be attributed to the insufficient amount of data used. It is discovered that the spillover effects of trading volumes across the study areas can be exploited to improve the predictive accuracy, and that the diversity of the predicted values from the candidate models can be used to increase the forecasting",7.0,
937404ec1229cc64fcc294843ee42350fd92560c,10.1108/jpif-08-2019-0102,The influence of a mortgage interest and capital deduction policy on house prices,2020.0,,Wouter Vangeel; Laurens Defau; L. De Moor,"PurposeSince 2005, Belgian housing prices have strongly increased. As the timing coincides with the implementation of a new fiscal package in order to stimulate homeownership, our study attempts to provide an understanding whether the mortgage interest and capital deduction (MICPD) policy has had the side-effect of increasing housing prices while, at the same time, controlling for key housing price determinants.Design/methodology/approachA fixed-effects regression model is used on a panel dataset of the three Belgian regions over the period 1995–2015.FindingsEstimations are carried out separately for different house types, being useful as our empirical analysis ascertains a significant price-increasing effect for ordinary houses and apartments but a significant price-reducing effect for villas. In addition, we find, among other things, that interest rates' influence has been less substantial than commonly thought.Originality/valueThese results are relevant for all governments willing to stimulate homeownership through fiscal stimuli.",7.0,https://biblio.vub.ac.be/vubirfiles/75849151/paper_MICPD_final.pdf
87378638f6f7d0ea6b932d009cc5d2729102e593,10.5539/ass.v8n10p16,Stress Testing of Commercial Banks' Exposure to Credit Risk: A Study Based on Write-off Nonperforming Loans,2012.0,,Wei Lu; Zhiwei Yang,"This study introduced a stress-testing model with a dummy variable that refers to write-off non-performing loans (NPL) by Agricultural Bank of China. A new variable Y that indicated the rate of NPL in major national commercial banks in terms of logit transformation was applied to test stress tolerance. This article built a regression model on the basis of four explanation variables: the growth rate of GDP, indicator of customer price, the growth rate of supplying nominal currency and indicator of house price. Then we took advantages of VAR model to establish the relationship between variables. Based on the model, diverse scenario was set up to conduct stress test to NPL of commercial banks. The test covered four quarters and discovered that lower growth rate of GDP, slump in CPI, slowdown in supply of nominal currency and surging price of house are in charge of short-term increase in non-performing loans. From long-term perspective, the commercial banks would initiate internal system to mitigate the shock from volatile macro factors.",7.0,https://ccsenet.org/journal/index.php/ass/article/download/19316/12778
7caee4aadfe955fa3cd0c4e6c37861eadda7ede6,10.11113/jt.v75.5236,RELATIONSHIP BETWEEN HOUSING AFFORDABILITY AND HOUSE OWNERSHIP IN PENANG,2015.0,,N. Sani,"There are many variables affected housing affordability. The variables are household income, household expenditure, monthly household payment, type of work, level of education, number of children, house price, housing location etc. These variables have been used as an indicator for future housing buyer before they make a decision to own a house. The objectives of this research are to determine the relationship between housing affordability and house ownership. The sample of this research comprises individuals who own a house at Flat Setia Vista, Relau, Pulau Pinang. The convenience sampling technique is used for this research with Binary Logistic Regression method as its analytical tool. The findings indicate that from six variables were tested in this research, namely household income, household expenditure, monthly household payment, type of work, level of education and number of children, only two variables have a relationship of housing affordability in housing ownership in Penang such as household income and number of children.",7.0,https://doi.org/10.11113/jt.v75.5236
3a290c46b59dbe3bee9eb1e3767475540cc443b7,10.1080/00036846.2024.2301976,"Liquidity constraints and speculation: Evidence from the housing-resale restriction policy in Qingdao, China",2024.0,Applied Economics,Xiaodong Wang; Sha Yu; Li Zhang,"ABSTRACT This article seeks to explore the efficacy of the Housing-Resale Restriction (HRR) policy in curtailing housing speculation, complementing existing literature that primarily examines the impact of taxation. The HRR policy directly reduces speculative opportunities through heightened liquidity constraints. Employing the methodology of regression discontinuity in time (RDiT), this study analyzes the impact of liquidity constraints on housing price growth rates based on the monthly community-level data from Qingdao, China, where the HRR policy was the most stringent in terms of scope and holding time requirement. The results show that the HRR policy reduced housing price growth rates by 1.3881 to 2.1001% points on average. Moreover, the disincentive impact was less pronounced in affluent, recently developed communities and those near subway stations. These findings provide valuable insights for policymakers to regulate the real estate market, mitigate excessive speculation, and prevent market bubbles.",7.0,https://www.tandfonline.com/doi/pdf/10.1080/00036846.2024.2301976?download=true
e76b723a135c0f89473101662e6fc9fd97d0977d,10.24912/jiksi.v13i1.32863,PERBANDINGAN KINERJA ALGORITMA EXTREME GRADIENT BOOSTING DAN RANDOM FOREST UNTUK PREDIKSI HARGA RUMAH DI JABODETABEK,2025.0,Jurnal Ilmu Komputer dan Sistem Informasi,Dhiwa Aqsha,"The demand for housing continues to increase along with population growth. Predicting house prices is crucial to assist prospective buyers and investors in making more informed decisions. This study aims to predict house prices in the Jabodetabek area by comparing the performance of two machine learning algorithms, namely Extreme Gradient Boosting and Random Forest, to produce accurate price estimates. The prediction process includes data preprocessing, key variable exploration, and model evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²). The results show that the Random Forest model performs best, with an MAE of 95,200,513.25, an MSE of 1.47e+19, and an R² of 0.77, outperforming the Extreme Gradient Boosting model with an MAE of 121,836,703.27, an MSE of 3.03e+19, and an R² of 0.52. Thus, this research is expected to serve as an effective tool for stakeholders in mitigating risks in property investment decisions in the Jabodetabek area.",7.0,https://journal.untar.ac.id/index.php/jiksi/article/download/32863/19320
c5eff9499d055f5e031e7bf0b46aeb4ab8dcdb14,10.54097/bdfe8032,A Comparative Study of Random Forest Regression for Predicting House Prices Using,2024.0,Highlights in Science Engineering and Technology,M. Mao,"Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.",7.0,https://drpress.org/ojs/index.php/HSET/article/download/18543/18082
c4ee95a98b9526e6c775066ad12a5e8dcc31fa2e,10.1109/cscloud-edgecom58631.2023.00025,A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method,2023.0,International Conference on Cyber Security and Cloud Computing,Zhenyu Yang; Xinghui Zhu; Yangcong Zhang; Peng Nie; Xin-Bo Liu,"The growth of real estate sector has been significantly influenced in recent years by ongoing regulation of real estate acquisition policy and the effects of the COVID-19 epidemic on economy. The fluctuation in housing prices is one of the most concerning factors for prospective homeowners. Whether property prices can sustain a comparatively constant level for an extended period of time is a crucial factor for prospective homeowners. Numerous modeling and application techniques for prediction algorithms, together with the promotion of machine learning algorithms, offer fresh approaches to forecast residential real estate values. This work proposes a novel stacking ensemble learning method (DStacking) which is based on the diversity of learners including XGBoost and BP neural network. Through the application of ensemble learning algorithm, D-Stacking method can successfully predict the possible promotion of housing price. Housing price datasets from China and the USA were used in the experiments to guarantee the generalizability of findings. Experimental findings indicate that the diversity of base learners significantly affects the predictive power of D-Stacking method. Furthermore",7.0,
faa1d688377647cbb692f2d2d7e64eee396ff6d5,10.35741/issn.0258-2724.57.5.45,POST-PANDEMIC ANALYSIS OF HOUSE PRICE PREDICTION IN SURABAYA: A MACHINE LEARNING APPROACH,2022.0,Journal of Southwest Jiaotong University,Trianggoro Wiradinata; Felicia Graciella; Rinabi Tanamal; Y. Soekamto; Theresia Ratih Dewi Saputri,"The COVID-19 outbreak caused a slowdown in the Indonesian economy, as it did in many other impacted nations. Consequently, the housing market in Indonesia, along with other industries, deteriorated. Other post-pandemic issues displace the property industry's priorities in Indonesia. Determining a fair property price is a problem occurring because of the economic slowdown. Property sellers expected their property selling prices to be the same before the pandemic or even increase, but property agents hoped the properties would be selling fast, creating a sense of distrust between the seller and the property agents. This work aims to develop a machine learning-based prediction model for real estate agents to use in determining property prices, with the expectation that the resulting predictions will be more accurate and supported by the data, increasing seller and buyer confidence. Following the suggestion from previous studies, several supervised algorithms such as Linear Regression, Decision Tree, and Random Forest were used to develop the model. Training data were collected from five property agents in Surabaya and as well as web scraping from the online home sales portals. Finding",7.0,
988702e701cefa5c41597ef5db70876a7f8ef7d7,10.2991/aebmr.k.220307.380,Macroeconomic Factors Affecting Housing Prices: Take the United States as an Example,2022.0,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Xinying Ding,"Residential real estate takes the largest part of asset market in the United States. This study investigates how changes in macroeconomic variables affect changes in housing prices, using time series data from 191 observation samples in the United States over the past 15 years. The dataset was collected from FRED and analysed by Stata/IC 16.1. Based on the model of multiple linear regression, the main results show that stock growth and economic growth are the major indicators of the rise of the housing price index. In contrast, mortgage rates and unemployment rates will have a negative effect on housing price. It is worth noting that in the regression model with 5% significance level, population growth as a determinant is not statistically significant. Compared with the existing literature, the main contribution of this paper is the relatively updated data in recent severe environments (Great Depression and COVID-19) to reinforce key discoveries.",7.0,https://www.atlantis-press.com/article/125971723.pdf
d2b0ab77175d7715a021363e1b6ea755fa5a7d67,10.3390/su14020973,"Does an Urban Wilderness Promote Gentrification? A Case Study from Knoxville, Tennessee, USA",2022.0,Sustainability,Jilleah G. Welch; Charles B. Sims; M. McKinney,"The Knoxville Urban Wilderness (KUW) is a successful example of a growing global movement to utilize vacant urban land as many cities “de-urbanize”. A key question is whether this particular kind of green space promotes social inequality via green gentrification. Our analysis shows how the KUW has affected nearby home prices. Socioeconomic data including income, educational attainment, and race is also presented to explore the possibility of gentrification in South Knoxville. Our findings do not support strong evidence of gentrification, which implies that lower-income households are benefiting from advances in environmental amenities. Other households in specific areas are benefiting from both increases in home values and from expansions of the KUW. These are encouraging results for urban planning efforts that seek to utilize large areas of vacant urban land while also having positive social and economic impacts.",7.0,https://www.mdpi.com/2071-1050/14/2/973/pdf?version=1642242980
6912b0e080d411078b058c3f942b219bcf1d9d72,10.2139/ssrn.3852817,What Drives Investors to Chase Returns?,2021.0,Social Science Research Network,Valentina Michelangeli; J. Huntley; Felix Reichling,"We use data on one-participant retirement savings plans to identify a behavioral bias in savings decisions. Investors who earn top-decile returns increase contributions to their accounts more than other investors. Using characteristics of the investors, characteristics of their retirement savings accounts, and multivariate regression analysis, we first show that such ``return chasing'' behavior is robust to controls for financial illiteracy, macroeconomic conditions, learning, transaction costs, housing prices, and informational frictions. We then use a structural two-asset model with tax-deferred and taxable assets to show that a permanent increase in expected returns produces investment responses for younger or liquidity-constrained investors that are consistent with our data. Our results provide evidence that younger investors' recent portfolio experiences have highly persistent effects on their expectations.",7.0,https://doi.org/10.2139/ssrn.3852817
cd19ddf7eaaf394a62a98d9d83e3a4c7d145761f,10.1108/jerer-06-2020-0037,Do segregated housing markets have a spillover effect on housing prices in nearby residential areas?,2021.0,,Mohammad Ismail; Abukar Warsame; Mats Wilhelmsson,"From an international perspective, housing segregation in Sweden is considered relatively low. However, in recent years the issue has been raised and problematized. For example, some studies show that ethnic housing segregation increased in 199 of Sweden's 285 municipalities during the years 2005- 2015. The purpose of this project is to analyze the trends regarding housing segregation over the past 10-20 years, and whether housing segregation has a spillover effect on neighboring housing areas. Namely, does proximity to a specific type of segregated housing market has a negative impact, while another type of segregation has a positive impact, on nearby housing markets. The results indicate that segregation measured as income sorting has increased over time in some of the housing markets. Its effects on housing values in neighboring housing areas are significant and statistically significant.",7.0,https://www.emerald.com/insight/content/doi/10.1108/JERER-06-2020-0037/full/pdf?title=do-segregated-housing-markets-have-a-spillover-effect-on-housing-prices-in-nearby-residential-areas
758a901297c082af4ca00b813f701fa1fbdeabc7,10.26877/asset.v6i3.602,Improving the Accuracy of House Price Prediction using Catboost Regression with Random Search Hyperparameter Tuning: A Comparative Analysis,2024.0,"Advance Sustainable Science, Engineering and Technology",Faezal Hartono; Muljono Muljono; A. Fanani,"Achieving a significant improvement over traditional models, this study presents a novel approach to house price prediction through the integration of Catboost Regression and Random Search Hyperparameter Tuning. By applying these advanced machine learning techniques to the King County Dataset, we conducted a thorough regression analysis and predictive modeling that resulted in a marked increase in accuracy. The baseline model, a conventional linear regression, provided a foundation for comparison, evaluating performance metrics such as R-squared and Mean Squared Error (MSE). The meticulous hyperparameter tuning of the Catboost model yielded a remarkable improvement in predictive accuracy, demonstrating the efficacy of sophisticated data science techniques in real estate and property valuation. The percentage increase in accuracy over the baseline model is explicitly stated in the abstract.",7.0,https://doi.org/10.26877/asset.v6i3.602
b5a8c3df663b85ec70ba2d9ee5bcb99d011d927e,10.54097/hset.v39i.6549,"The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China",2023.0,Highlights in Science Engineering and Technology,Chengke Zou,"House prices increase substantially in China from 1998. Because of expensive house prices, most Chinese people have only one chance to select suitable houses. Therefore, building a house price prediction model based on housing conditions is significant for customers to make decisions. This paper collects the estate market data of Jinan city from the HomeLink website and performs several feature selection algorithms to get critical features for house price prediction. The paper compares the classical machine learning methods for the problem, including Multiple Linear Regression, Random Forest, and Catboost. After cross-validation tests, the CatBoost, algorithm with the lowest Mean Square Error (MSE) is regarded as the most accurate algorithm to predict house prices. The analytic results show that the house price is dominated by the location features such as area and block.",7.0,https://drpress.org/ojs/index.php/HSET/article/download/6549/6344
bb585e3b1bd1ac160b05cf31245217236fd66b46,10.1080/08882746.2019.1601928,"House price impacts of construction quality and level of maintenance on a regional housing market: Evidence from King County, Washington",2019.0,Housing and Society,S. Mathur,"ABSTRACT Accurately estimating the monetary value of the construction quality and the level of maintenance of a house is a significant gap in house price research. Such estimates would be valuable for various actors in the housing market. Homebuyers would benefit from this information during the bidding process. Homeowners would benefit from it during renovation—particularly when financing their homes with a home equity line of credit—and when deciding on the need for renovation before selling their homes. Property value assessors would be able to provide better estimates of property values, and developers might make better decisions regarding the quality of new construction projects. Finally, lending institutions and public-sector housing agencies would benefit when providing loans or grants for housing rehabilitation and renovation.This research employs spatial econometric regression modeling and finds that construction quality and level of maintenance significantly affect house prices. For example, a medium-quality house sells for approximately 25% more than a low-quality house, and a well-maintained house sells for approximately 5% more than a house that is not well-maintained.",7.0,
eeedb48b06f3660101615bf3de6b27e1951880ee,10.1017/age.2021.24,"Impact of public water supply unreliability on residential property prices in Marion County, West Virginia",2021.0,Agricultural and Resource Economics Review,Fahad Alzahrani; A. Collins,"Abstract Water supply unreliability in many public water systems stems from aging infrastructure. We measure unreliability by the issuance of boil water notices (BWNs) within one year prior to single-family residential sale observations. Using a spatial quantile regression framework on transactions between 2012 and 2017, we find statistically significant, negative relationships between BWNs and residential properties. The estimated impacts of unreliability on residential housing prices, however, are not uniform across the distribution of prices. Specifically, we find that BWNs have a larger impact on medium- to low-priced houses (at or below the 60 percent quantile) compared with high-priced houses. An aggregate marginal willingness-to-pay value of $4.2 million was computed for a one-day reduction in annual BWN throughout Marion County.",7.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/870098F7E578CD506796D4EB169A8094/S1068280521000241a.pdf/div-class-title-impact-of-public-water-supply-unreliability-on-residential-property-prices-in-marion-county-west-virginia-div.pdf
b2c0dfc88e3d9fd9f04842764cd9e032d5f39423,10.1080/10511482.2023.2254749,"Different Data, Different Measures: Comparing Alternative Indicators of Changes in Neighborhood Home Values",2023.0,Housing Policy Debate,Dan Immergluck; A. Hollis,"Abstract Urban scholars and practitioners have used changes in neighborhood-level home values to serve as indicators of neighborhood change, including gentrification and disinvestment. A common measure is the “median home value” variable from the American Community Survey (ACS). However, household-level research suggests that self-assessed home values, such as those of the ACS, differ significantly from market-based measures, and medians can be affected by changes in the mix of homes . Transaction-based home price indices are unaffected by such changes and are based on market sales rather than self-assessments, but also have limitations. Moreover, self-assessments of home values might be desired if the intention is to measure the value households place on their homes or to avoid potential biases baked into market values. Comparing changes in the ACS median home value to a common market-based home price index (HPI), we find that the ACS median tends to fall more slowly than the HPI when values are falling and increase more slowly than the HPI when values rise. The differences between the measures are large and are not randomly distributed across space, tending to be larger in neighb",7.0,
ab196df26194b3225a5a0975cd51ad710f5d37ae,10.3846/1648715x.2013.822031,Spatio-temporal analysis of house price convergence based on a demographical distance,2013.0,,Le Ma; Chunlu Liu,"Convergences of house prices have been studied for over three decades, but yet have been confirmed because of spatial heterogeneity and autocorrelations in house prices. A spatio-temporal approach was recently proposed to address the spatial and temporal issues related to house prices. However, most previous studies placed the focus on the spatial heterogeneity and autocorrelations from geographical locations, which neglected other spatial factors. In order to overcome this shortfall, this research argued a demographical distance, constructed by demographical structure and housing market scales, to investigate the house price convergences in Australian capital cities. The results confirmed the house price levels in Canberra, Brisbane and Perth converged to the house price level in Sydney.",7.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/4199/3562
c975137190a72c5375447530819a02db2a0d6fbe,10.1109/csde56538.2022.10089222,Deep Learning for Real Estate Trading,2022.0,2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE),Yun Zhao; Girija Chetty; Dat Tran,"Artificial intelligence (AI) and Machine Learning techniques have been making impact on the real estate industry recently. Increasingly, several real estate companies have started to use a variety of AI techniques to optimize their property business. Ma-chine learning (ML) technology for providing support on real es-tate investment decisions, allows investigation of historical property sales data by computer algorithms to automatically predict house prices. Real estate professionals can leverage sophisticate ML techniques to analyse sales data as benchmarks and make appraisals for their home selling clients and customers. ML technologies not only make predictions or classifications, but also can assistant real estate professionals for investment purposes by providing a trading strategy. In this paper, we propose a novel machine learning model, based on a standard deep reinforcement learning (DRL) model, enhanced with a combination of two popular time series algorithms, the Gramian Angular Field (GAF) and long short-term memory (LSTM) algorithms for providing decision support on real estate trading strategy. Our goal is to explore if the proposed enhanced DRL model can make profitab",7.0,
852acacfeb2b48a4a5c2a0be7e04ecdda05d9f07,10.1007/s12205-021-1250-z,Comparative Analysis of Spatial Impact of Living Social Overhead Capital on Housing Price by Residential type,2021.0,KSCE Journal of Civil Engineering,Yeol Choi; I. Jeung; JeJin Park,"We examined 15,446 single-family detached houses and 2,494 apartment houses in Saha-gu, Busan Metropolitan City, to see whether the accessibility to social overhead capital (living SOC), influences housing prices spatially. Living SOC represents different types of essential facilities. We divided houses into single-family detached houses and apartment houses to perform a more accurate analysis. We used a geographically weighted regression (GWR) model for local-level analysis with a 200-m grid and 400-m grid as spatial units to consider spatial effects. The GWR model explained the price variation better than an ordinary least squares (OLS) model. The results provide a variety of implications to consider when establishing a business-and-action plan based on living SOC.",7.0,https://doi.org/10.1007/s12205-021-1250-z
cf13f86bf0a960f63bcc18e02e9eaa08bc2c5b73,10.1109/iciscois56541.2023.10100566,Optimization Techniques for Deep Learning Based House Price Prediction,2023.0,"2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)",J. Vijava; Pangoth Santhosh Kumar; Meetiksha Sorgile; Murukuri SV Sv Vasanth,"With the persisting increment in the rates of houses, the cost of homes is escalating every year. So it is necessary to design a system that can predict future housing prices. This intelligent system undertakes the task to play a crucial role between learning and optimization. But because these systems are dynamic, complex, and multidisciplinary, optimizing them is particularly challenging. The House Price Index (HPI) is one of the most useful indicators for calculating frequent changes in home prices. The data processing of the current home pricing involves several approaches and takes into account several variables, including housing design, layouts, climate control conditions, etc. To incorporate the benefits of external factors and utilize the increased home price, this approach makes use of a multiple regression model and optimization approaches. Using enhanced housing price composition and multiple regression prediction models, such as particle swarm optimization, genetic, firefly, and mayfly algorithms, etc., it is possible to include the benefits of outside influences. The purpose of this work is to provide an overview of the latest and most popular deep learning optimizati",7.0,
501f1410f163a308d7b6a092d2adffec19465ac2,10.1111/joie.12291,Social learning and local consumption amenities: Evidence from Yelp*,2022.0,Journal of Industrial Economics,Elliot Anenberg; Chun Kuang; Edward Kung,"Using data from Yelp, we show that consumers learn about restaurant quality from reviews, which means restaurants are more likely to go out of business when receiving poor reviews. Average restaurant quality thus becomes higher in areas with faster learning, which tend to be areas closer to the city center, and areas with younger and more educated populations. To quantify the effect of learning on equilibrium restaurant quality, we estimate a Bayesian learning model of consumer demand and restaurant exit. Simulations show that learning increases average restaurant quality by 0.25 Yelp stars in large markets and by 0.11 stars in small markets. This differential increase in restaurant quality associated with learning accounts for 0.9 percentage points of the house price difference between such areas. Our results have implications for the literature on gentrification and urban revival. JEL Codes: D83, L15 ∗The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. †Board of Governors of the Federal Reserve System ‡Department of Economics, East Carolina University. §Department of Econ",7.0,
634bec8b578e4be9738d8fe68e463bb89ac76ce7,10.18488/journal.11/2012.1.2/11.2.31.44,The Impact of Neighborhood Characteristics on Housing Prices-An Application of Hierarchical Linear Modeling,2012.0,International Journal of Management and Sustainability,Chun-Chang Lee; H. Lin,"Housing data are of a nested nature as houses are nested in a village, a town, or a county. This study thus applies HLM (hierarchical linear modelling) in an empirical study by adding neighborhood characteristic variables into the model for consideration. Using the housing data of 31 neighborhoods in the Taipei area as analysis samples and three HLM sub-models, this study discusses the impact of neighborhood characteristics on house prices. The empirical results indicate that the impact of various neighborhood characteristics on average housing prices is different and that the impact of house characteristics on house prices is also moderated by neighborhood characteristics.",7.0,https://doi.org/10.18488/journal.11/2012.1.2/11.2.31.44
f252380207b85f2c07f3ae2c70817bd93310a211,10.1080/00343404.2019.1619925,Does infrastructure spending lead to price effects in the property market? Evidence from major cities across India,2019.0,Regional studies,Xiaoying Deng; Anupam Nanda; S. Ong,"ABSTRACT A recent boost in spending on the public infrastructure across India raises the question: How does the property sector react to massive infrastructure construction? The answer has significant policy relevance. This paper uses panel data across seven major Indian cities, spanning seven years from 2008 to 2014, to ascertain the short- and long-run impacts of infrastructure attributes on property values and rents. The study finds that increases in the percentage of public infrastructure spending lead to a positive effect on the property capitalization rate in India, but have a negative impact on the rental market in the short term.",7.0,https://centaur.reading.ac.uk/83708/3/CRES-2018-0260_XD_AN_SO_accepted.pdf
9c241e331ee1f36bf77f74eb1b29d12a817fbca5,10.1093/jrsssc/qlad001,The determinants of Airbnb prices in New York City: a spatial quantile regression approach,2023.0,"Journal of the Royal Statistical Society, Series C: Applied Statistics",M. Bernardi; M. Guidolin,"
 In this paper, we study the price determinants of Airbnb rentals, for the case of New York City, by developing a new dataset, which combines attributes of the property and of the related service, with other information available as open data. This dataset is employed within a spatial quantile semiparametric regression model, able to handle the intrinsic heterogeneity of house prices. The results confirm that property and service attributes play a significant role in determining rental prices, while some variables exert a different impact on prices in magnitude and sign, depending on the quantile considered.",7.0,https://hdl.handle.net/11577/3467556
ee29e8f8dc538ea83411050af0d9177cdbad9b4d,10.1109/icmsci62561.2025.10893972,Unveiling House Price with Machine Learning Algorithm,2025.0,2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI),Rambabu Kusuma; Kalagadda Kiran Kumar; Siva Ramakrishna Pillutla; A. Saikumar,"A frequently overlooked and yet very critical factor in having a high quality of life is the perfecting of one's living environment. A house should be quiet, unique, and instill peace in the soul of its residents, attaining individual needs across many aspects. Therefore, finding such a house with great architecture and making the right choice is essential. Beyond location, the structure and cost of a house are key elements that significantly influence the suitability of a place to live. Accurate price predictions are crucial to address potential future concerns. This study presents a model designed to help individuals select an ideal home based on their financial circumstances. It evaluates various machine learning models, including the Random Forest Regressor and Gradient Boosting Regressor, to ensure reliable predictions. The proposed model aims to present estimates as closely as possible aligned with ideal conditions, considering individual preferences and demands.",7.0,
99bc7ac9ccb7813da35fe003ff6e7e077196a041,10.1016/j.econlet.2020.109687,Anchoring in surveys of household expectations,2020.0,,Erik Hjalmarsson; P. Österholm,"Abstract We assess whether the concept of anchoring – where a respondent’s answer is affected by the reference number stated in the survey question – is related to the characteristics of the respondent. Using a survey of household expectations of Swedish housing prices, we find significant differences in observed anchoring across groups reflecting age, income, homeownership and sex. Implications for the interpretation of survey results are discussed.",7.0,https://www.sciencedirect.com/science/article/pii/S016517652030447X/pdf
b92ac1251e29013dc2fbf276d3317cad843dd278,10.1109/aisc56616.2023.10085264,Using Machine Learning to Predict Housing Prices,2023.0,Artificial Intelligence and Symbolic Computation,H. Prakash; Kshitij Kanaujia; Sapna Juneja,"Apartment prices are projected based on various apartment attributes such as location, square footage, population, latitude, and longitude. Real estate is generally seen as an investment vehicle for the modern consumer, which is reflected in the volatility of real estate prices and the impact of real estate markets on the global economy. A literature review is conducted to analyze relevant properties and the most efficient models for predicting property prices. The results of this paper demonstrate the effectiveness of machine learning techniques such as linear regression in predicting accurate prices. Our results also suggest that location and structural characteristics are important factors in predicting house prices. The study identifies key attributes used by property developers and researchers to determine property prices, and is particularly helpful in identifying the best machine learning models for conducting research in this area.",7.0,
e7de23fdbf68fe5930da5eac02236f86018d72c6,10.1109/icscde54196.2021.00012,A Novel Deep Neural Network based Method for House Price Prediction,2021.0,2021 International Conference of Social Computing and Digital Economy (ICSCDE),Jiayi Xu,"Nowadays, house price is extremely essential for human living and plays an important part in the financial market, especially in the real estate industry. In this paper, we proposed a deep learning based network for house price prediction. Firstly, a brief introduction and related work are discussed. Then, Linear Regression, Random Forest Regression, XGBoost, and SVM are all detailed explained, developed and tested. In addition, several types of densely connected based neural network are proposed and developed. Finally, all methods are evaluated on a publicly available dataset, Boston house price dataset. Our method performs competitively performance compared with other classical methods including Linear Regression, Random Forest Regression, XGBoost, and SVM, and evaluated with a variety of evaluation methods including R2, Adjustable R2, MAE, MSE, RMSE.",7.0,
9506ae1fbe9151332d35795cdaa05259db376dcc,10.1109/aidas47888.2019.8970916,Automated Machine Learning based on Genetic Programming: a case study on a real house pricing dataset,2019.0,2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS),S. Masrom; T. Mohd; Nur Syafiqah Jamil; Abdullah Sani Abdul Rahman; Norhayati Baharun,"Designing an effective machine learning model for prediction or classification problem is a tedious endeavor. Significant time and expertise are needed to customize the model for a specific problem. A significant way to reduce the complicated design is by using Automated Machine Learning (AML) that can intelligently optimize the best pipeline suitable for a problem or dataset. This paper demonstrates the utilization of an AML that has been developed with a meta-heuristic algorithm namely Genetic Programming (GP). Empirical experiment has been conducted to test the performances of AML on a real dataset of house prices in the area of Petaling Jaya, Selangor. The results show that the AML with GP able to produce the best pipeline of machine learning with high score of accuracy and minimal error. (Abstract)",7.0,
c4ec945f03e2d427bc119a1dea692dea9e06917b,10.1177/0739456x221119817,"Jobs-Housing Imbalances, Urban Segregation, and Intra-metropolitan Commute Flows in Mexico City",2022.0,Journal of planning education and research,D. Bautista-Hernández,"This study uses data from a comprehensive travel survey in Mexico City to explore the relationship between the jobs-housing ratio and self-contained commute at the TAZ (Traffic Analysis Zone) level. A hierarchical clustering approach identified job-rich and housing-rich areas. Gravity regression models explored the determinants of work trips to job-rich and from housing-rich communities separately. Results indicate jobs-housing ratio had a modest positive association with locally employed residents but not with self-containment. Housing prices in the workplace were highly positively associated with work trips, and therefore it is suggested as a barrier to the co-location process.",7.0,
266fbf57e3f931ee12c0a2f189676d152e2db050,10.1177/0739456x20912829,Higher Density Development for Lower Cost Housing? Understanding the Multifamily Housing Market and the Role of Density in Multifamily Home Prices,2020.0,Journal of planning education and research,Hong-Wei Dong,"This study documents the development and the characteristics of multifamily homes in the Portland metropolitan area and evaluate the roles of density and density-related factors in determining multifamily home prices. The study finds that medium-density multifamily homes sell at lower prices than other housing types in dollar per unit and dollar per square foot. The cost-saving effect of higher density development is weak or even negative, and dwelling size is the primary determinant of multifamily home prices. The effects of neighborhood density on home prices depend on housing types. Investors own almost a half of nonapartment multifamily homes and buy homes at lower prices.",7.0,
c33ba8238ad4b0bb55ca5075b23da0628531d732,10.1080/13504851.2019.1613488,Determining the effect of abnormal temperatures on the housing market,2020.0,Applied Economics Letters,Mitchell R. Livy,"ABSTRACT Extreme weather events have increased in frequency and are expected to grow in intensity over time. To examine perceptions of this abnormal weather, I model the relationship between degree days and the housing market. The results provide evidence that abnormal increases in cooling and heating degree days negatively affect housing prices, with cooling degree days having a larger impact. Adding to these estimates, the housing price capitalizations of heating and cooling related housing attributes are shown to vary with recent abnormal temperatures.",7.0,
e408c0b40841065d0b2eef1d581bfec02823f049,10.1177/0739456x19898737,"Impact of Heavy-rail-based Rapid Transit on House Prices: Evidence from the Fremont, CA, Warm Springs BART Extension Project",2020.0,Journal of planning education and research,S. Mathur,"This study estimates households’ willingness to pay for single-family houses and condominiums/townhouses located within 2 miles of Warm Springs (WS) BART Station in Fremont, CA. The study finds that, compared to the houses sold in the control distance band (2–5 miles away), an average-priced single-family house within 2 miles of the WS BART Station was higher in price by 9 to 15 percent. The total property value increment for the single-family houses is large enough to fund the $802 million Warm Springs BART Extension Project cost five times over.",7.0,
78753eb78a8f8440566608f39b7bfb9e12910aae,10.1007/s12061-020-09367-y,"Urban Housing Prices and Regional Integration: A Spatial Analysis in the City of Kaifeng, China",2020.0,Applied spatial analysis and policy,Yuanyuan Cai; Ying-Ming Zhu; Feng-Lin Yuan; Jin-Long Gao; M. Helbich,,7.0,https://link.springer.com/content/pdf/10.1007/s12061-020-09367-y.pdf
6c487bea2a2814c03d866b091cec343fd6039a6b,10.1016/j.landurbplan.2023.104838,"Estimating proximity effects to wildfire fuels treatments on house prices in Cibola National Forest, New Mexico, USA",2023.0,Landscape and Urban Planning,Ryan A. Fitch; Julie M. Mueller; James R. Meldrum; Christopher Huber,,7.0,https://doi.org/10.1016/j.landurbplan.2023.104838
287e461e7c01925f22341155132b3b9b157bf9ab,10.31462/jcemi.2021.02106116,House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model,2021.0,Journal of Construction Engineering Management & Innovation,Fatemeh Mostofi; Vedat Toğan; H. B. Başağa,,7.0,https://doi.org/10.31462/jcemi.2021.02106116
550f4cfd1d368ff54ed62baddf069097c531ccdb,10.19030/iber.v9i7.605,Forecasting real US house price : principal components versus Bayesian regressions,2010.0,,Bayesian Regressions; Rangan Gupta; A. Kabundi,,7.0,https://clutejournals.com/index.php/IBER/article/download/605/591
965f2234f38cd18c8f4d6363a914aef179db03b8,10.1016/j.infoecopol.2018.07.002,Information technology in the property market,2018.0,Information Economics and Policy,Yong Suk Lee; Yuya Sasaki,"Information technology is increasingly being utilized in the property market. This paper examines how sensitive house transaction prices are to online price estimates using data collected from Zillow. We find that online property price estimates strongly predict transaction prices even when observable and unobservable house and neighborhood characteristics are controlled for. In addition, we find evidence that suggests that online price estimates may have a direct impact on transaction prices.",7.0,
4346f8504f85ba6583335d3f37e0d08c1f0f19b6,10.1007/s11146-023-09956-x,Do Higher House Prices Crowd-Out or Crowd-In Manufacturing? A Spatial Econometrics Approach,2023.0,Journal of real estate finance and economics,Ping Feng; Mahmut Yasar; Jeffrey P. Cohen,,7.0,
e595f90da1c380b652dedd130adb7a3f462b1686,10.1016/j.frl.2024.106107,Detecting House Price Bubbles in G7 Countries: New Evidence and Heterogeneous Determinants,2024.0,Finance Research Letters,Xiao-Ming Zhang; Mengqing Zhu; Yi-Ming Tian; Stefano Zedda,,7.0,
f3d8fb09231a923339d673bdef8251853cc33271,10.16538/j.cnki.jfe.2016.07.012,"Real Estate Attributes, Income Gap and Change Trend of Housing Prices",2016.0,,Zhong-Fei Li; Shou-Dong Yu; Jun-Peng Zheng,,7.0,
afd97faa83868b9dfa1076408fe014055df02a69,10.1007/s41324-023-00565-7,"Examining the effect of apartment attributes on their sale prices in Riyadh, Saudi Arabia",2024.0,Spatial Information Research,Ali Chikhmous; Muhammad Tauhidur Rahman,,7.0,
6adb473abb925a5e83cd6278443aa09deffd7ee3,10.1108/ijhma-09-2024-0139,Drivers of housing financing in Kenya,2025.0,International Journal of Housing Markets and Analysis,H. M. Kilonzo; M. Muriithi; Benedicto Onkoba Ongeri,"Purpose
Housing finance is frequently difficult to provide in developing nations due to unstable macroeconomic conditions and a lack of supportive legal, technological and regulatory frameworks (Lea and Bernstein, 1996). Governments in these countries have, therefore, created a range of organizations and initiatives to improve the flow of capital to the housing market on a footing that is affordable to their populations given the household income levels (Ram and Needham, 2016). Housing, however, is by its very nature a significant investment requiring a considerable capital outlay at the onset (Dasgupta et al., 2014). This makes acquiring it challenging, particularly in underdeveloped nations where saving tendencies are quite low partly because of low-income levels (Keller and Mukudi-Omwami, 2017). As a result, many developing nations struggle with severe housing issues that lead to slums, overcrowding and related health issues.

Design/methodology/approach
The theoretical model for analyzing housing finance in Kenya in this study incorporates both demand and supply aspects, drawing from Brueckner’s (1994) framework. This model divides factors influencing demand into certainty and ",6.0,
cbc09b1a9eda30bea7dd2429ab563d1cbefc6b7c,10.2991/emeeit-15.2015.107,Analysis of Main Influence Factors for Housing Prices,2015.0,,Cong-Jun Rao; Y. Ge,"Keywords: Housing price; Analysis of main influence factors; multiple linear regression Abstract. Housing price problem is an important economic and social issue of widespread concern, which affects the life quality of urban residents and also affects the continued steady growth of national economy. Based on method of multiple linear regression analysis, this paper studies the relationship between housing prices and the influencing factors in the aspects of demand and supply, and identifies the main factors of influencing the housing prices. It tries to provide the valuable reference for the government to regulate the housing market effectively. The modern real estate industry has become a pillar industry of Chinese national economy, and the housing price is the key in the real estate industry. The housing price level is not only related to the living standards of the masses, but also related to the global economic development. However, in recent years, due to the cyclical and regional real estate development, there is imbalance in the market of supply and demand. Together with the increased raw material prices and costs, the house price is becoming more and higher. The housing pri",6.0,https://download.atlantis-press.com/article/25841966.pdf
337a3be923b9f47a8be2f2a80b1598ab6aca6910,10.1136/bmjopen-2024-087274,Identifying pre-diabetes ‘hotspots’ in Northern California using geospatial analysis: opportunities to target diabetes prevention strategies and improve health equity,2024.0,BMJ Open,Tainayah W. Thomas; O. Duru; Maher Yassin; Luis A Rodriguez; Tannaz Moin; Yelba M. Castellon-Lopez; Julie A. Schmittdiel,"Objectives The US Preventive Services Task Force recommends screening of adults aged 35–70 with a body mass index ≥25 kg/m2 for type 2 diabetes and referral of individuals who screen positive for pre-diabetes to evidence-based prevention strategies. The diabetes burden in the USA is predicted to triple by 2060 necessitating strategic diabetes prevention efforts, particularly in areas of highest need. This study aimed to identify pre-diabetes hotspots using geospatial mapping to inform targeted diabetes prevention strategies. A ‘hotspot’ is defined as a cluster of 3 or more neighbouring census tracts with elevated pre-diabetes prevalence. Design A cross-sectional study using ArcGIS software to geospatially map pre-diabetes prevalence hotspots. We used health system and census data to identify pre-diabetes hotspots using a systematic five-step geoprocessing approach that made use of incremental spatial autocorrelation and Getis-Ord Gi*. Setting This study was set in Kaiser Permanente Northern California (KPNC), an integrated health delivery system with over four million members. Participants KPNC adults ages 35–70 who underwent a haemoglobin A1c (HbA1c) or fasting plasma glucose (FPG",6.0,https://bmjopen.bmj.com/content/bmjopen/14/12/e087274.full.pdf
56494cbdae8af81697e839076186a1ac0cd07374,10.1108/jerer-01-2021-0002,The impact of crimes on house prices in LA County,2022.0,Journal of European Real Estate Research,Paloma Taltavull de La Paz; J. Berry; David McIlhatton; David Chapman; Katja Bergonzoli,"PurposeThis paper focusses on analysing the impact of crime on the housing market in Los Angeles (LA) County. By looking at different types of crime instead of general crime measures and controlling by spatial dimension of prices and crime as well as endogeneity, a model is developed that allows for the understanding of how a specific crime impacts the housing market transaction price. To perform the analysis, the paper merges different data sets (crime, housing transaction and census data) and then computes the distances to crucial transport modes to control the accessibility features affecting housing prices. The latter allows estimating the association of housing prices and crime in the distance and estimating the impact on housing depending on it.Design/methodology/approachThis paper focusses on the following crimes: aggravated assault, burglary (property crime), narcotics, non-aggravated assault and vandalism. The paper shows firstly how incidents of reported crime are distributed across space and how they are related to each other – thus highlighting crime models with spatial influences. Secondly, the research utilises instrumental variables within the methodology to estimate",6.0,https://pure.coventry.ac.uk/ws/files/52969002/Binder1.pdf
8d2c318441fe6d61e5a37021bc2908139d8fbc64,10.1001/jamasurg.2023.3939,Gentrification as a Factor in the Incidence of Firearm Injuries.,2023.0,JAMA Surgery,Sarabeth A Spitzer; Daniel G. Vail; P. Heindel; Tanujit Dey; Zara Cooper; Ali Salim; Molly P. Jarman,"Importance
Firearm injuries are an epidemic in the US; more than 45 000 fatal injuries were recorded in 2020 alone. Gaining a deeper understanding of socioeconomic factors that may contribute to increasing firearm injury rates is critical to prevent future injuries.


Objective
To explore whether neighborhood gentrification is associated with firearm injury incidence rates over time.


Design, Setting, and Participants
This cross-sectional study used nationwide, urban US Census tract-level data on gentrification between 2010 and 2019 and firearm injuries data collected between 2014 and 2019. All urban Census tracts, as defined by Rural Urban Commuting Area codes 1 to 3, were included in the analysis, for a total of 59 379 tracts examined from 2014 through 2019. Data were analyzed from January 2022 through April 2023.


Exposure
Gentrification, defined to be an area in a central city neighborhood with median housing prices appreciating over the median regional value and a median household income at or below the 40th percentile of the median regional household income and continuing for at least 2 consecutive years.


Main Outcomes and Measures
The number of firearm injuries, controll",6.0,https://www.frbsf.org/community-development/wp-content/uploads/sites/3/wp2015-05.pdf
b033134c36f6e43706d80a7aa388ff4f9afa7520,10.32996/jefas.2023.5.6.10,Understanding Negative Equity Trends in U.S. Housing Markets: A Machine Learning Approach to Predictive Analysis,2023.0,"Journal of Economics, Finance and Accounting Studies",Afrin Hoque Jui; Shah Alam; Md Nasiruddin; Adib Ahmed; Rashed Mohaimin; Md Khalilor Rahman; Farhana Rahman Anonna; Rabeya Akter,"In the intricate landscape of the U.S. housing market, negative equity has emerged as a significant concern for homeowners, lenders, and policymakers alike. This phenomenon, characterized by homeowners owing more on their mortgages than the current value of their homes, can have far-reaching economic and social implications.  The main goal of this research project was to develop machine learning models that can effectively predict negative equity trends in U.S. housing markets. This involved a multi-faceted approach that encompasses data collection, model development, and validation to ensure the accuracy and reliability of predictions. The historical housing market data used for this research covers various regions across the United States, from urban to suburban and rural, to provide diversified dynamics in the markets. The dataset utilized for this analysis comprises a comprehensive collection of variables relevant to understanding negative equity trends in the U.S. housing market. It includes historical housing prices, which reflect property values across various regions, mortgage rates that provide insights into borrowing costs, and key economic indicators such as employment r",6.0,https://al-kindipublisher.com/index.php/jefas/article/download/8660/7361
f845f4f170b902741f2f15df1362f7ba759bb1cb,10.1108/ijhma-09-2021-0105,Determinants of house prices in Malaysia,2022.0,International Journal of Housing Markets and Analysis,Camara Alpha Kabine,"
Purpose
This paper aims to empirically examine the relationship between macroeconomic factors and housing prices in Malaysia from 1991 to 2016.


Design/methodology/approach
This study was based on annual macroeconomic data from 1991 to 2016. For the model that was constructed, house prices were treated as the dependent variable and other variables, namely, the interest rate, the gross domestic product (GDP) and the consumer price index (CPI) as explanatory or regression variables. All these data come from the annual publication of the World Bank’s statistical report. In the meantime, data on house prices have been obtained through the publication of the National Property Information Centre in the form of an index. In the study, the bounds testing approach for cointegration was used because the Engel Granger cointegration tests can be used only if all the series are stationary at first difference. And it is against this background the author could not use it because our variables are a combination of series integrated of orders I (0) and I (1).


Findings
The results of this study revealed a positive equilibrium relationship in the long run between interest rate, CPI and housing p",6.0,
b5305495553d75174bc5226c64eb35f78f16db04,10.1111/cico.12459,Epilogue: Lessons from the Sociology of Small Cities and Other Understudied Locales,2020.0,,Japonica Brown–Saracino,"My sociological imagination was ignited in a small New England town, complete with rolling hills, farm fields, and forest, a three-classroom-schoolhouse, Methodist church, general store, and Town Hall. On the one hand, the town was seemingly removed from some of the defining characteristics of the United States in the 1980s; indeed, on a good day, our television antenna captured two stations and the nearest mall was more than thirty miles away. On the other hand, many of the same cultural, economic, and social forces that influenced the nearest big cities of Boston and New York marked my little town. Gentrification, in the form of the in-movement of college educated back-to-thelanders seeking the rural idyll via establishing organic farms and gathering at secluded swimming holes, shaped debates on the Town Hall floor and helped to produce rising home values and property tax bills. Meanwhile, the increasing power of corporate agriculture meant that the small dairy farms that dotted the landscape in my early years closed one by one; today, only one family dairy farm remains in operation. Here, the dual processes of decline and revitalization that characterized so many places in the 1",6.0,
9fdf009cff897bc1331833e5180ac591faa0a9fa,10.1108/wje-12-2023-0504,e-Waste in construction: a comprehensive bibliometric analysis and review of the literature,2024.0,World Journal of Engineering,Vineet Kumar; D. Verma,"Purpose
The global construction industry faces both challenges and opportunities from electronic waste (e-waste). This study aims to present a bibliometric analysis and comprehensive literature assessment on e-waste in concrete construction materials.

Design/methodology/approach
This study studies 4,122 Scopus documents to examine garbage generation in different countries and inventive ways to integrate e-waste into construction as a sustainable strategy. This study lists famous researchers and their cooperation networks, demonstrating a robust and dynamic area with a surge in research output, notably from 2018 to 2022. Data is visually represented using VOS Viewer to show trends, patterns and study interests throughout time.

Findings
The findings imply that e-waste can improve construction materials’ mechanical characteristics and sustainability. The results are inconsistent and suggest further optimization. e-Waste into construction has garnered scientific interest for its environmental, life cycle, and economic impacts. This field has great potential for improving e-waste material use, developing sophisticated prediction models, studying environmental implications, economic an",6.0,
cb11f9f56c9495e9207e7fd73f3ee935f94e1ff8,10.3390/su17125495,"Parks and People: Spatial and Social Equity Inquiry in Shanghai, China",2025.0,Sustainability,Xi Peng; Xiang Yin,"Urban parks are essential public resources that contribute significantly to residents’ well-being. However, disparities in the spatial distribution and social benefits of urban parks remain a pressing issue. This study focuses on the central urban area of Shanghai, a representative high-density megacity, and its findings hold significant reference value for similar cities, systematically evaluating urban park services from the perspectives of accessibility, spatial equity, and social equity. Leveraging multi-source big data and enhanced analytical methods, this study examines disparities and spatial mismatches in park services. By incorporating dynamic data, such as actual visitor attendance and residents’ travel preferences, and improving analytical models, such as an enhanced Gaussian two-step floating catchment area method and spatial lag regression models, this research significantly improves the accuracy and reliability of its findings. Key findings include (1) significant variations in accessibility exist across different types of parks, with regional and city parks offering better accessibility compared to pocket parks and community parks. (2) Park resources are unevenly dis",6.0,https://doi.org/10.3390/su17125495
a907bca8d93164bc6a58954d25c4d949e6fcbc6e,10.3390/su151511641,"Supermarket Store Locations as a Proxy for Neighbourhood Health, Wellbeing, and Wealth",2023.0,Sustainability,Ilya Ilyankou; Andy Newing; Nick Hood,"The “Waitrose effect” captures the notion that the presence of stores operated by Waitrose, an upmarket UK grocer, increases the value of nearby real estate. This paper considers the broader relationship between Waitrose store locations and neighbourhood type by comparing the health and wealth of neighbourhoods with and without access to Waitrose stores in England. Whilst we do not seek to imply causality, we demonstrate better health, wellbeing, and wealth in neighbourhoods falling within a Waitrose store catchment. In those neighbourhoods, median home prices were almost 2.5 times higher (in urban neighbourhoods) compared to neighbourhoods served only by other major grocers, which formed our control groups. Neighbourhoods in Waitrose catchment areas fare better on indicators of health too. In urban neighbourhoods falling within a Waitrose store catchment (accounting for 98% of Waitrose catchment neighbourhoods), residents are more likely to self-report very good health than those in our largest control groups. The prevalence of mood and anxiety disorders is also significantly lower in those neighbourhoods than in the control groups. Our findings strongly suggest that the presence ",6.0,https://www.mdpi.com/2071-1050/15/15/11641/pdf?version=1690535087
44784adc8638d5ab0117abff6d97bec1b534e8cf,10.1007/s11356-023-26666-2,"Exploring the association of Brownfield remediation status with socioeconomic conditions in Wayne County, MI",2023.0,Environmental science and pollution research international,B. O'Leary; Alex B. Hill; Colleen Linn; Mei Lu; Carol J. Miller; Andrew Newman; F. G. Sperone; Qiong Zhang,"Urban neighborhoods with locations of environmental contamination, known as brownfields, impact entire neighborhoods, but corrective environmental remedial action on brownfields is often tracked on an individual property basis, neglecting the larger neighborhood-level impact. This study addresses this impact by examining spatial differences between brownfields with unmitigated environmental concerns (open site) and sites that are considered fully mitigated or closed in urban neighborhoods (closed site) on the US census tract scale in Wayne County, MI. Michigan’s Department of Environment, Great Lakes, and Energy’s leaking underground storage tank (LUST) database provided brownfield information for Wayne County. Local indicators of spatial association (LISA) produced maps of spatial clustering and outliers. A McNemar’s test demonstrated significant discordances in LISA categories between LUST open and closed sites ( p  < 0.001). Geographically weighted regressions (GWR) evaluated the association between open and closed site spatial density (open-closed) with socioeconomic variables (population density, proportion of White or Black residents, proportion of college educated population",6.0,https://link.springer.com/content/pdf/10.1007/s11356-023-26666-2.pdf
c8d7cf372496fb213ca7847401570d38582338d2,10.1108/pm-12-2015-0063,Housing cycles in the UK: a historical and empirical investigation,2017.0,Property Management,Charalambos Pitros; Yusuf Arayici,"
Purpose
The study looks at the characteristics of upswings and downswings for UK housing cycles. Specifically, the purpose of this paper is to empirically analyse cycles in house prices and housing affordability on the characteristics of persistence, magnitude and severity.


Design/methodology/approach
The paper draws upon the triangular methodology of cycles and utilises housing data from the last three decades.


Findings
From an empirical perspective, the study obtained four main results. First, the graphical trajectory of cycles in house price and housing affordability is highly synchronized. Second, upturns in both cycles tend to be longer than downturns on average. Third, the recent upturn in house prices and housing affordability is characterised by larger duration, magnitude and severity than the earlier case. Fourth, the latest downturn in both cycles is highly synchronised in terms of time occurrence, persistence, magnitude and severity; in addition, in both cases, the latest downturn is considerably smaller than the previous one. The study additionally indicates that on average the length of a complete house price and housing affordability cycle is 19 years on a peak-t",6.0,http://nrl.northumbria.ac.uk/id/eprint/43357/1/Housing%20cycles%20in%20the%20UK%20a%20historical%20and%20empirical%20investigation.pdf
9095003ffc06eca53d0fe6110393fdd6b400fe3e,10.1145/3369114.3369125,Combining Domain Knowledge & Machine Learning: Making Predictions using Boosting Techniques,2019.0,International Conference on Advances in Artificial Intelligence,Utku Kubilay Cinar,"The latest hit on technology is the information and telecommunication novelties. Internet and big data are important source of information in order to understand this vast majority of the upcoming knowledge. Websites are progressively expanding and making it available to everyone who has access. Modern economic systems are built on data or knowledge. Thus, tech companies gather huge data and exercise their powers to digitalize the information to capture and utilize the knowledge within their reach. Information management enables firms to improve customer satisfaction, increase revenue, understand customer behavior, mitigate risk assessment, making a multidisciplinary approach. Another approach is to be able to identify business strategies. Information management researches (using machine learning algorithm) can help the field to discover what is significant on a customer behavior and reduce costs to its clients. Over the recent years, information management and machine learning algorithms are getting more and more close and on topic. Since, information management and machine learning intensely concerns with domain knowledge. Artificial Intelligence allows the machines to accumulate",6.0,
3ecdf7609271fea5608cc5d2bbcdbd86bdadfd8f,10.3390/ijerph192315558,"Disparity and Spatial Heterogeneity of the Correlation between Street Centrality and Land Use Intensity in Jinan, China",2022.0,International Journal of Environmental Research and Public Health,G. Yin; Tian Liu; Yan-Bin Chen; Yi-Ming Hou,"In the existing literature on the correlation between street centrality and land use intensity (LUI), only a few studies have explored the disparity of this correlation for different types of LUI and the differences across various locations. In response to the above shortcomings, in this study, the main urban area of Jinan, China, was taken as an example, and the disparity and spatial heterogeneity of the correlation between street centrality and LUI were explored for different categories of land use. The multiple centrality assessment (MCA) model was used to calculate the closeness centrality, betweenness centrality, and straightness centrality of the traffic network. Based on the floor area ratio (FAR) of each parcel, the utilization intensities of the residential, industrial, commercial, and public service land uses were measured. Employing the kernel density estimation (KDE) method, the street centrality of the traffic network vis-à-vis the urban LUI was rasterized into the same spatial analysis framework. The Pearson correlation coefficient and geographically weighted regression (GWR) were used to measure the correlation between the two variables and the spatial heterogeneity ",6.0,https://www.mdpi.com/1660-4601/19/23/15558/pdf?version=1669209588
1e91e28b3280421b3cdeee9899a68c1361c74375,10.15826/vopr_onom.2020.17.3.043,Toponymic Image-Building for Urban Locations,2020.0,,M. Golomidova,"The paper looks into the specifics of urban place naming from the perspective of modern territory marketing and branding. Regarding the image of a place as a strategy, the author postulates the importance of toponymic image-building for the market economy and transformation of the socio-cultural milieu. The study focuses on the cases of urban naming aimed at managing the perception of spatial objects and supported through their specific media representation. Most of these place names belong to Ekaterinburg, although some references to other cities are made as well. The originality of the proposed research consists in using discourse analysis to explain the image-building capacities of place naming that relate both to the name choice and to the semiotics of the material landscape, as well as to the intents and purposes of stakeholders involved in the promotion process. Toponymic image-building techniques are commonly used for positioning and promotion of residential real estate, prestigious business facilities, leisure and sports grounds. This enables the author to outline the naming techniques associated with different spheres of social relations. It is noted that toponymic image-b",6.0,https://doi.org/10.15826/vopr_onom.2020.17.3.043
7fe9a8a74405e6cde8ab02fa8c5b37b29e7d37b2,10.1093/geroni/igab012,Booms and Busts in Housing Market and Health Outcomes for Older Americans,2021.0,Innovation in aging,Da-Hai Yue; N. Ponce,"Abstract Background and Objectives The US housing market has experienced considerable fluctuations over the last decades. This study aimed to investigate the impacts of housing price dynamics on physical health, mental health, and health-related behaviors for older American outright owners, mortgaged owners, and renters. Research Design and Methods We drew longitudinal data from the 1992–2016 Health and Retirement Study and merged it to the 5-digit zip code–level Housing Price Index. The analytic sample comprised 34 182 persons and 174 759 person-year observations. We used a fixed-effects model to identify the health impacts of housing price dynamics separately for outright owners, mortgaged owners, and renters. Results A 100% increase in Housing Price Index was associated with a 2.81 and 3.50 percentage points (pp) increase in the probability of reporting excellent/very good/good health status for mortgage owners and renters, respectively. It was also related to a lower likelihood of obesity (1.82 pp) for outright owners and a lesser chance of obesity (2.85 pp) and smoking (3.03 pp) for renters. All of these relationships were statistically significant (p < .05). Renters also expe",6.0,https://academic.oup.com/innovateage/article-pdf/5/2/igab012/38247641/igab012.pdf
e16f8b3f5504168f65400bdccdb0fe5471728fdd,10.3390/info16121049,Artificial Intelligence and Real Estate Valuation: The Design and Implementation of a Multimodal Model,2025.0,Inf.,Nikolaos Karanikolas; Eleni Kyriakidou; Eleni Athanasouli,"The valuation of real estate is a fundamental process for the proper functioning of the market and the formulation of public policies. The mother of the traditional methodologies is the comparative method, which is based on data that is often incomplete or unreliable, especially in real estate markets with limited transparency. In contrast, online advertisements offer a wealth of unstructured information, which requires advanced analysis techniques. Even if they reflect price demands rather than real estate’s final market values, they provide a wealth of data that is particularly useful for market transparency. Real estate advertisements are also enriched with photographs and videos of the property. This paper proposes a methodological framework that integrates artificial intelligence techniques (natural language processing and computer vision) to extract structured features from text and photographs of advertisements. The resulting dataset feeds the comparative method of property valuation, applied to thousands of properties. Geographic analysis and neighborhood characteristics of properties enrich the proposed algorithm, covering all the available geodata that affect real estate ",6.0,https://www.mdpi.com/2078-2489/16/12/1049/pdf?version=1764667660
88e4303ab44af2b9a635da7c01a34209174ab551,10.1016/j.ssmph.2021.100963,"Spatial distribution of neighborhood-level housing prices and its association with all-cause mortality in Seoul, Korea (2013–2018): A spatial panel data analysis",2020.0,SSM: Population Health,Ikhan Kim,"Housing prices are known to be a relevant indicator of the socioeconomic position of the neighborhood. In a society where the market system mainly drives housing prices, residents' spatial patterning is formulated according to their socioeconomic position. Dividing the 2013–2018 entire study period into three periods, we explored the spatial distribution of housing prices and all-cause mortality and their association in Seoul, the country's capital city. The government authorities' data and 2015 census data were used for the study. We mapped the spatial distribution of housing prices and all-cause mortality and investigated the changes in distribution. We conducted a pooled ordinary least square (OLS) and spatial panel regression analysis to estimate housing prices elasticity of all-cause mortality. We also explored the possible mediating role of housing prices on the educational composition's effect on all-cause mortality. We found the common trends of increasing spatial patterning of housing prices and all-cause mortality. The magnitude of spatial patterning was far greater in housing prices than all-cause mortality. A pooled OLS regression analysis found that a 1% increase in ho",6.0,https://www.sciencedirect.com/science/article/pii/S235282732100238X/pdf
3cdd5d905644ffb220fb5c9b9cb6071706d01d31,10.1080/13467581.2024.2373830,Impact of sudden public crises on spatial distribution patterns and driving factors of the urban catering industry: a case study of Shanghai’s catering POI data before and after COVID-19,2024.0,Journal of Asian Architecture and Building Engineering,Dan-Dan Shao; Kyungjin Zoh; Yanzhao Xie,"ABSTRACT This study uses Shanghai to explore the effects of the COVID-19 pandemic on the spatial distribution patterns and driving factors of the urban catering industry. Through quantitative analysis of restaurant point-of-interest data and influencing factors in Shanghai’s main urban area from 2016 to 2022 using geographic information technology, machine learning, and spatial econometric models, this study predicts spatial changes in the catering industry. The number of restaurants in Shanghai’s main urban area continuously decreased from 2016 to 2022, with a significant pre-pandemic decline. The catering industry in this area has a significant spatial correlation in the spatial distribution, showing a pattern of “one core dominance with multiple cores coexisting,” with Jing’an and Huangpu Districts having the highest densities, which is expected to continue. Target markets remain a decisive factor in restaurant location selection post-COVID. Restaurant aggregation is most favorable when the population density is between 0 –50,000 people/km2, GDP is between 0–20 billion yuan/km2, housing prices are between 20,000 –130,000 yuan/m2, school density is between 0–20/km2, hospital dens",6.0,https://doi.org/10.1080/13467581.2024.2373830
e214bad02ba010faf5d9c3edf93dffcb558da3f2,10.3390/su12187622,Organization of the Building Space of Developments and Its Impact on Residential Housing Prices,2020.0,Sustainability,Edward Preweda; E. Jasińska,"This study has examined the process of urban space development in Krakow (southern Poland) using two developments as an example. The planning documents were analysed and spatial data obtained from the CAPAP (Centre for Spatial Analysis of Public Administration) project were processed. The data source for analysis comprised 2D outlines of certain buildings from the topographic object database (BDOT10k), elevation data from lidar aerial laser scanning at a density of 12 points per square meter and a numerical terrain model (NMT) with a one-meter mesh grid. In addition, existing models of the boundaries of the plots including their numbers and the ownership structure were used. On this basis, a digital mock-up of the analysed site was created. The issue surrounding the purchasing of these sites is interesting, since on the primary market, the apartments are bought long before the start of the first construction works. For this reason, the buyer is not necessarily aware of how the buildings surrounding his/her property will ultimately look, including whether or not they will obscure the sun’s rays. Until now, there has been no need to analyse the density of buildings, as there was no d",6.0,https://www.mdpi.com/2071-1050/12/18/7622/pdf?version=1600245465
42ba305d4c89d2757f2bbe4c16bb9dc82a49c883,10.3390/land13050577,Assessment of the Spatial Variation of the Economic Benefits of Urban Green Spaces in a Highly Urbanized Area,2024.0,Land,C. Cho; Kwangil Cheon; Wan-Mo Kang,"Urban green spaces play a vital role in improving the quality of life and well-being of urban residents. However, their economic benefits in different spatial contexts within highly urbanized areas remain a critical yet understudied topic. This study delves into the economic value of urban green spaces in Cheongju City, Republic of Korea, and investigates the distance-decay features associated with the proximity of green spaces to residential properties. Two spatial econometric models were employed to address these questions: the spatially autoregressive (SAR) model and the generalized additive model (GAM). The SAR model was used to assess the economic benefits of urban green spaces, whereas the distance decay of these benefits was examined with the GAM. Empirical analyses revealed that small-sized parks or forests under 20 ha hold greater economic value when in proximity to residential areas compared to medium-sized parks or forests between 20 and 200 ha. Conversely, large parks or forests over 200 ha appeared to have a disamenity effect, negatively impacting property prices when in close proximity. The GAM’s smooth functions illustrated that the distance-decay effect was shorter ",6.0,https://www.mdpi.com/2073-445X/13/5/577/pdf?version=1714139016
9fdd03c1504c10587859693c9ffb24b73d795a9e,10.2139/ssrn.2495411,Transformations of industrial heritage: Insights into external effects on house prices,2014.0,,M. van Duijn; J. Rouwendal; Richard Boersema,"Local policy makers seek ways to deal with abandoned industrial heritage in their jurisdictions. Much is demolished, but in some cases considerable investments are made to preserve the cultural aspects of industrial sites. The renewal plans are usually designed to stimulate urban renewal in the vicinity of these sites. Little seems to be known about the effectiveness of these policies and in this paper, we study whether the redevelopment of five industrial heritage sites caused positive external effects by investigating the development of house prices in nearby residential areas. We use a quasi‐experimental design by comparing quality‐adjusted house prices before the start, between the start and the completion and after the completion of the transformation. We find substantial effects of one site, which is the best-known example of renovated industrial heritage in the Netherlands, but much smaller or no effects for the other sites. We also model the decay of these effects over time and space. We find different decay effects for each case. We conclude that industrial heritage sites do not necessarily cause negative external effects. If there are negative external effects present the",6.0,https://eres.architexturez.net/system/files/pdf/eres2014_59.content.pdf
eae1fa4ec6c484c49ce8dd326e72c864d666b498,10.1080/19475683.2025.2523739,Enhancing the computational efficiency of the SGWR model and introducing its software implementation,2025.0,Ann. GIS,M. Lessani; Zhen-Long Li,"ABSTRACT The SGWR model is a local spatial regression model that functions similarly to Geographically Weighted Regression (GWR) but incorporates a spatial weight matrix derived from both geographical proximity and data attributes similarity. The integration of these two weights is controlled by a parameter (alpha), which determines their respective contributions to the regression. While the previous SGWR model outperforms GWR statistically, it remains computationally expensive, particularly during alpha optimization. In this study, we improved the model’s computational efficiency in two ways: (1) optimizing the steps used to calculate the Corrected Akaike Information Criterion (AICc) for both alpha optimization and model fitting, and (2) implementing Message Passing Interface (MPI) for parallel processing. Additionally, we developed a Python package that implements both sequential and parallel versions of the model, along with a tool to enhance accessibility. Furthermore, we incorporated a bi-square kernel function alongside gaussian kernel function, allowing for greater flexibility in spatial weighting and model adaptation to different data structures. The experiment results illu",6.0,https://doi.org/10.1080/19475683.2025.2523739
4f8107aff5823e5fe4f9f6b68e189318ad920658,10.3390/buildings12081197,Influences of the Plot Area and Floor Area Ratio of Residential Quarters on the Housing Vacancy Rate: A Case Study of the Guangzhou Metropolitan Area in China,2022.0,Buildings,Xiao-Li Yue; Yang Wang; Hong'ou Zhang,"Factors affecting the housing vacancy rate (HVR) vary, but few studies have considered the relationships between the HVR and plot area (PA) and floor area ratio (FAR). This study thus considered 212 residential quarters in the Guangzhou metropolitan area as the research object, and we constructed a regression model of the factors impacting housing vacancies. The model includes two explanatory variables, PA and FAR, and the remaining six impact factors as control variables. In this study, the influences of PA and FAR on the HVR was analyzed by combining the traditional ordinary least squares (OLS) and two spatial regression models: the spatial lag model (SLM) and spatial error model (SEM). The results indicate that (1) the HVR in the Guangzhou metropolitan area shows spatial difference characteristics of the low central area and high edge, and there is spatial autocorrelation. (2) The PA of the residential quarters gradually increases from the central to the edge area, but the spatial pattern of FAR is the opposite. (3) The SLM results indicate that the PA and FAR of the residential quarters have significant positive correlations with HVR; that is, the larger the PA and FAR, the lar",6.0,https://www.mdpi.com/2075-5309/12/8/1197/pdf?version=1660198969
f3395ef15815e2214034f12ebdc28c7285867a3a,10.1080/15387216.2024.2425308,Leave or stay? The determinants of the migration behavior of university graduates in Shanghai,2024.0,Eurasian geography and economics,Yunjiang Yu; Jianfa Shen; Guang-Yi Xin; Jun He,"ABSTRACT Millions of university graduates in China either stay in the cities where they go to universities (for their bachelor’s degree) or migrate to other areas each year. What factors determine their destination choice? This paper investigates migration patterns of university graduates from Shanghai among the university city, hometown, and other cities, and the roles of general factors and special Chinese factors such as hukou in graduate mobility. The main findings are as follows. First, key factors that attract graduates to stay in the university city, return to their hometown, or go to other cities are different and one factor may not have the same impact. Second, there are specific spatial patterns of migration from different types of hometowns to three destination types, as different types of hometowns offer different life experiences as well as opportunities in the spatial administrative system. There is a strong preference for Chinese graduates to move up the urban hierarchy to bigger cities than their hometowns. Third, the impacts of some general factors such as housing price, social capital, and human capital are similar to those of Western countries. The impacts of huk",6.0,https://www.tandfonline.com/doi/pdf/10.1080/15387216.2024.2425308?needAccess=true
cc15e0dfd4c098e06ddf94c44d79f227912a68f2,10.24148/wp2023-17,Climate Change and the Geography of the U.S. Economy,2023.0,"Federal Reserve Bank of San Francisco, Working Paper Series",S. Leduc; Daniel J. Wilson,"This paper examines how the spatial distribution of people and jobs in the United States has been and will be impacted by climate change. Using novel county-level weather data from 1951 to 2020, we estimate the longer-run effects of weather on local population, employment, wages, and house prices using a panel distributed lag model. The historical results point to long-lasting negative effects of extreme temperatures on each of these outcomes. We highlight that a long lag structure is necessary to appropriately capture the longer-run effects of climate change, as short-run effects are often small and imprecisely estimated. Using county-level weather projections based on alternative greenhouse gas emissions scenarios, we use the estimated models to project the spatial distribution of these local economic outcomes out to 2050. The results point to substantial reallocations of people and jobs across the country over the next three decades, with mobility increasing by between 33 and 100 percent depending on the scenario. Population and employment are projected to shift away from the Sunbelt and toward the North and Mountain West. We document that this would, in fact, be a continuation ",6.0,https://www.frbsf.org/wp-content/uploads/sites/4/wp2023-17.pdf
97faf0425bd93478994383f40b54ddfb3298cba3,10.1007/s40864-024-00229-1,Quantifying the Social Equity of Economic Performance for Different Groups of Residents in Rail Transit Station Areas,2024.0,Urban Rail Transit,Tianyue Wan; Wei Lu; X. Na,"In response to the global trend of urbanization, there has been an increasing focus on transit-oriented development (TOD). However, the prioritization of economic factors in the establishment of TOD often takes precedence over concerns for social equity. This research seeks to address this gap by examining the economic performance and demographic characteristics of 46 rail transit station areas (RSAs) in the city center of Dalian. The study employs the Gini coefficient and affinity clustering to assess the overall economic performance and inequality among different resident groups within RSAs. Furthermore, regression analysis is utilized to identify the key variables influencing economic performance equity in these areas. The results indicate significant disparities in economic dimensions among different resident groups, with station areas in commercial centers and functional core zones demonstrating higher economic performance. Housing prices and job–housing density are identified as crucial factors influencing consumer behavior across various station areas. Despite the presence of employment opportunities and urban development features in RSAs, differences in socioeconomic status",6.0,https://doi.org/10.1007/s40864-024-00229-1
dc47571c5b54e8fcfd84e029450ec61f0f66c303,10.3390/ijgi13040112,Measuring the Spatial-Temporal Heterogeneity of Helplessness Sentiment and Its Built Environment Determinants during the COVID-19 Quarantines: A Case Study in Shanghai,2024.0,ISPRS Int. J. Geo Inf.,Yuhao He; Qianlong Zhao; Shanqi Sun; Wen-Jing Li; Waishan Qiu,"The COVID-19 outbreak followed by the strict citywide lockdown in Shanghai has sparked negative emotion surges on social media platforms in 2022. This research aims to investigate the spatial–temporal heterogeneity of a unique emotion (helplessness) and its built environment determinants. First, we scraped about twenty thousand Weibo posts and utilized their sentiments with natural language processing (NLP) to extract helplessness emotion and investigated its spatial–temporal variations. Second, we tested whether “helplessness” was related with urban environment attributes when other real estate economic and demographic variables were controlled using the ordinary least squares (OLS) model. Our results confirmed that helplessness emotion peaked in early April when the lockdown started. Second, residents in neighborhoods characterized by higher rents and property management fees, higher population density, lower housing prices, lower plot ratios, or surrounded by less tree view and higher perceived visual complexity, are found to exhibit higher degree of “helplessness”. This study provides an effective data-driven framework to utilize social media data for public sentiments monitori",6.0,https://www.mdpi.com/2220-9964/13/4/112/pdf?version=1711536767
7bc91ca7aac577f7ff42e5d87963d6c8d911b665,10.3389/fbuil.2021.813399,"Role of the Built Environment in the Recovery From COVID-19: Evidence From a GIS-Based Natural Experiment on the City Blocks in Wuhan, China",2022.0,Frontiers in Built Environment,Maosu Li; Yi Peng; Yi-Jie Wu; Jin-Ying Xu; T. Tan; Hui Guo; Wei-Sheng Lu; A. Yeh; Fan Xue,"The built environment closely relates to the development of COVID-19 and post-disaster recovery. Nevertheless, few studies examine its impacts on the recovery stage and corresponding urban development strategies. This study examines the built environment’s role in Wuhan’s recovery at the city block level through a natural experiment. We first aggregated eight built environmental characteristics (BECs) of 192 city blocks from the perspectives of density, infrastructure supply, and socioeconomic environment; then, the BECs were associated with the recovery rates at the same city blocks, based on the public “COVID-19-free” reports of about 7,100 communities over the recovery stages. The results showed that three BECs, i.e., “number of nearby designated hospitals,” “green ratio,” and “housing price” had significant associations with Wuhan’s recovery when the strict control measures were implemented. At the first time of reporting, more significant associations were also found with “average building age,” “neighborhood facility development level,” and “facility management level.” In contrast, no associations were found for “controlled residential land-use intensity” and “plot ratio” thr",6.0,https://doi.org/10.3389/fbuil.2021.813399
b470391c84b290e7cb605a3cb407e2cf0411c916,10.3390/jrfm16020073,A Natural Quasi-Experiment of the Monetary Policy Shocks on the Housing Markets of New Zealand during COVID-19,2023.0,Journal of Risk and Financial Management,Chung Yim Edward Yiu,"It is hard to experimentally test the impacts of monetary policy shocks on housing markets as it is very unlikely for a central bank to change monetary policies swiftly twice within a short period of time for exogenous reasons. However, during the pandemic, the central bank of New Zealand changed its policies 180 degree in 2 years, from an unprecedented low interest rate and a relaxed mortgage policy in 2020 to a 13-year record high interest rate and a tightened mortgage policy in 2022. Among the OECD members, New Zealand is the country that increased the interest rate the earliest and also the country that had its house prices fall the earliest. It provides natural quasi-experiments to test the monetary policy hypothesis empirically by the two policy changes as treatments on house prices. This study conducts a time series regression analysis on the housing markets of New Zealand to test the hypothesis in the pre-COVID and the COVID periods, ranging from 2016 Q2 to 2022 Q3. The results confirm that mortgage rates have a negative and significant effect on house price changes after controlling for the economic growth factor and the housing supply factor, no matter whether the monetar",6.0,https://www.mdpi.com/1911-8074/16/2/73/pdf?version=1676971910
95ababec66c3036bf01889120701ed9327673ce0,10.3390/land11020259,Quantifying the Spatial-Temporal Variation of Population Urbanization and Affordable Housing Land in China,2022.0,Land,Chunyan He; Ding Li; Jun-Lin Yu,"The governments of most countries are striving to coordinate residents’ housing demands with the supply of land to achieve high-quality urbanization and improve residents’ welfare. Based on the panel data of all Chinese provinces from 2010 to 2017, this study obtains 248 observations. It uses the coupling coordination degree, the Mann–Kendall trend test, and spatial autocorrelation methods to analyze the temporal and spatial variation of the coordination degree of population urbanization and affordable housing land supply. The study aims to clarify the temporal trend further and identify the spatial mismatch of affordable housing land resource allocation and apply a panel fixed model to determine its driving factors. The regional differentiation of the coordinated development level of population urbanization and affordable housing land in China’s provinces is apparent. The overall spatial distribution characteristics of coordination are opposite to the development of its economic level. The Mann–Kendall trend test indicated that the coupling coordination degree had a significant upward trend nationwide. Moreover, the coupling coordination degree shows a strong positive global spati",6.0,https://www.mdpi.com/2073-445X/11/2/259/pdf?version=1645599783
303e04942037b7fc2fc530828eb5ab99016dd3ec,10.31893/multiscience.2023ss0206,Predicting future housing prices: a machine learning approach,2023.0,Multidisciplinary Science Journal,P. Verma; Satyendra Arya; Chetana Asbe,"In our ecosystem, the real estate sector is the least transparent. Daily changes in housing prices as well as sometimes exaggerated prices rather than valuations are a part of life in the housing market. Our research project's major focus is on predicting future housing prices using actual machine learning. Here, we want to concentrate our judgments on each fundamental factor that goes into calculating the price. Machine learning has played a significant role in picture identification, spam restructuring, normal speech command, product suggestion, and medical diagnosis in recent years. The current machine learning method aids us in improving security warnings, maintaining public safety, and improving medicinal advancements. Machine learning technology also improves customer service and makes automobiles safer. The current research discusses the prediction of future housing prices provided by a machine learning system. The data was preprocessed after it was collected. In this procedure, we employ the Forest Neural Gradient Boosting Algorithm (FNGBA). We evaluate and compare several prediction techniques for the selection of prediction methods. Our findings demonstrate the necessity ",6.0,https://malque.pub/ojs/index.php/msj/article/download/1249/627
ca144f14e7eda372b76b59491326ad60b868d753,10.1057/s41599-024-03728-5,Entrance opportunity vs. academic performance: unpacking the nonlinear capitalization effects of multidimensional school qualities on housing sales and rental prices,2024.0,Humanities and Social Sciences Communications,Li-Rong Hu; Shen-Jing He,"It is widely documented that school quality has a capitalization effect on housing prices. However, existing literature has commonly reported a linear relationship between housing sales prices and the academic performance of nearby schools. This study challenges this prevalent understanding by examining how the entrance opportunity as an integral part of school quality affects both housing sales prices and housing rental prices, in response to the newly adopted point-based school enrolment policy in China whereby tenants are allowed to access local public schools but with fewer opportunities compared to homeowners. Employing machine learning algorithms, this study identifies which key school indicators and what ranges of these indicators have significant impacts on different housing sales and rental price segments in Shenzhen, China. Our results reveal that housing rental prices, particularly in the lower-priced segment, are more determined by the entrance opportunity than the academic performance, while the latter is the most important factor influencing housing sales prices. The nonlinear capitalization effect suggests that tenants and homebuyers are willing to pay more for schoo",6.0,https://www.nature.com/articles/s41599-024-03728-5.pdf
b50bcfae5c5711c4bec621921cb3f613699ca294,10.1108/jerer-02-2019-0003,The search and matching process in the housing market,2019.0,Journal of European Real Estate Research,G. Lisi,"PurposeThis paper aims to study the phenomenon known as “house price dispersion”, one of the most important distinctive features of housing markets. House price dispersion refers to the phenomenon of selling two houses with very similar attributes and in near locations at the same time but at very different prices.Design/methodology/approachThis theoretical paper makes use of a search and matching model of the housing market. The search and matching models are the benchmark models of the “matching” markets, such as the labour market and the housing market, where trade is a decentralised, uncoordinated and time-consuming economic activity.FindingsUnlike the previous related literature that attributes to the heterogeneity of buyers and sellers a significant part of the price volatility, in this paper, the house price dispersion depends on the housing tenure status of home-seekers in the house search process. Indeed, in the presence of different housing tenure status of home-seekers, the house search process leads to different types of matching. In turn, this implies different surpluses (the sum of the net gains of the parties involved in the trade), and eventually, different surpluse",6.0,
e5091b49f06a0d3d6b13e819cb810effb10ea465,10.1108/sef-03-2019-0124,Real effects of real estate: evidence from unemployment rates,2020.0,,Can Dogan; John C. Topuz,"This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level.,This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument.,The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs.,This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate ",6.0,
dd2785313906142a3c87a8f90aad691ae1288c14,10.3390/buildings13071603,Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing,2023.0,Buildings,P. De Paola; Simone Previtera; B. Manganelli; F. Forte; F. D. Del Giudice,"Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weight",6.0,https://www.mdpi.com/2075-5309/13/7/1603/pdf?version=1687680724
3bc91d035defcabcc896f89c0c25215537aacc8b,10.1371/journal.pone.0273718,"Comparing forms of neighborhood instability as predictors of violence in Richmond, VA",2022.0,PLoS ONE,S. West; Diane L. Bishop; Derek Chapman; Nicholas D. Thomson,"Violence events tend to cluster together geospatially. Various features of communities and their residents have been highlighted as explanations for such clustering in the literature. One reliable correlate of violence is neighborhood instability. Research on neighborhood instability indicates that such instability can be measured as property tax delinquency, yet no known work has contrasted external and internal sources of instability in predicting neighborhood violence. To this end we collected data on violence events, company and personal property tax delinquency, population density, race, income, food stamps, and alcohol outlets for each of Richmond, Virginia’s 148 neighborhoods. We constructed and compared ordinary least-squares (OLS) to geographically weighted regression (GWR) models before constructing a final algorithm-selected GWR model. Our results indicated that the tax delinquency of company-owned properties (e.g., rental homes, apartments) was the only variable in our model (R2 = 0.62) that was associated with violence in all but four Richmond neighborhoods. We replicated this analysis using violence data from a later point in time which yielded largely identical resul",6.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0273718&type=printable
7263680a1a038cb537cd2718946e24114b8ae1f2,10.3390/land13040519,Effects of Urban Parks on Housing Prices in the Post-COVID-19 Pandemic Era in China,2024.0,Land,Xinxin Yang; Q. Ye; You Peng; Shaobo Liu; Tao Feng,"Urban parks are important for improving the quality of living environments. Although the impact of parks on housing prices has been well documented, the effects of the COVID-19 pandemic remain vague. This paper analyzes the housing prices of neighborhoods around Meixi Lake park in Changsha, at the initial stage of the COVID-19 pandemic in June 2020 and the stable stage of the recovery period in June 2023, which demonstrates the impacts of urban parks on property pricing and housing choice based on residential transaction data. The results indicate that urban parks are given a high priority in determining people’s choices. In June 2020, the risk of epidemic transmission and noise interference lowered the price of property that is close to public parks and recreational facilities. However, good management and social services in residential areas increase housing prices. However, when the pandemic ended, the strong demand for outdoor activities led to a price rise in the properties near Meixi Lake park. People are most likely to choose houses in a neighborhood characterized by good educational facilities and a high-quality living environment. More specially, the houses with a short di",6.0,https://www.mdpi.com/2073-445X/13/4/519/pdf?version=1713087539
a9e7b0e77edc93bfa2ce0c8358d0cc677291305a,10.3390/su13137419,Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel,2021.0,Sustainability,Jincheng Wang; Qun-Qi Wu; Feng Mao; Yi-Long Ren; Zilin Chen; Yaqun Gao,"Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts",6.0,https://www.mdpi.com/2071-1050/13/13/7419/pdf?version=1625217650
d1f63ff45bdb8672552a8577fe4bd9e702ec174d,10.53894/ijirss.v7i3.3220,Real estate price forecasting utilizing recurrent neural networks incorporating genetic algorithms,2024.0,International journal of innovative research and scientific studies,Ting-Tin Tin; Cheok Jia Wei; Ong-Tzi Min; B. Feng; Too Chin Xian,"This study aims to develop and examine the effectiveness of the Recurrent Neural Network (RNN) model incorporating Genetic Algorithm (GA) in forecasting real estate prices. Real estate prices have a significant impact on a country’s financial system. Therefore, the ability to accurately forecast its price is valuable. A set of data containing 5.4 million unique records of real estate with their prices is used in the study. The data set, which spans from 2018 to 2021, contains twelve independent variables and one dependent variable. We preprocessed the data set to reduce noise and outliers that could potentially lead to poor model performance. The RNN model was selected because (GA) optimises the hyperparameters in the Recurrent Neural Network (RNN) hidden layer to optimise the performance of the RNN model. Relative Root Mean Squared Error (RRMSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) examine the effectiveness of the RNN-GA model.The results show that the RNN-GA outperforms RNN and the traditional statistical models used in the real estate industry. This study provides an understanding of the effectiveness of incorporati",6.0,https://doi.org/10.53894/ijirss.v7i3.3220
0556a5599546a67e6954aa723774cdfafac335d8,10.1080/24694452.2025.2495075,Multimodal Accessibility and the Capitalization of Realized Access by Car and Transit in Housing Prices Across Canada,2025.0,Annals of the American Association of Geographers,Christopher D. Higgins; R. N. Arku; Steve Farber; Eric J. Miller,"Although the transportation accessibility benefits afforded by multiple transportation modes play a fundamental role in determining transportation costs and land prices, there are challenges associated with isolating how multimodal accessibility benefits are capitalized into property values. In response, this research extends calibrated gravity-based measures of potential accessibility to employment by car and transit to include mode share proportions for commuting to reflect how this accessibility potential is realized. These realized accessibility measures capture not only the presence but also the performance of different modes for facilitating commuting trips. Spatial econometric models examine the extent to which access is capitalized into property values across Canada’s twelve largest metropolitan regions. Results suggest that car and transit access potential is generally valued across most cities, although there is evidence of moderate multicollinearity between the unweighted car and transit access potential scores. In contrast, the origin and destination mode use weighted measures of realized access offer advantages in terms of model performance and interpretation. These mo",6.0,https://doi.org/10.1080/24694452.2025.2495075
52e57a1a55769261329615cd2052c48bd9bd1b38,10.1177/23998083221149424,"Would people prefer city-center living in the post-COVID era?: Experience, status, and attitudes to social disasters",2023.0,Environment and Planning B Urban Analytics and City Science,Kiseong Jeong; Jaebin Lim,"The COVID-19 pandemic was a significant social disaster that radically affected the paradigm of current urbanization and city-center living. Responses to the disaster varied depending on related experiences, individual status, and attitudes. Thus, this research extends the previous literature by examining the effects of experiences related to the COVID-19 pandemic, socioeconomic status, and how perceptions and attitudes affect preferences for city-center living in the Seoul Metropolitan Area, South Korea. We use data from PSSRAC (Perception Survey of Seoul metropolitan area Residential Awareness during COVID-19) of 2021. A binary logistic regression method is used to examine significant characteristics that affected the residential preference change due to “Experience,” “Status,” and “Attitude” in the COVID-19 era. The findings showed that respondents’ experience, status, and attitude related to the pandemic could have a complex effect on predictions of preference, for central or suburban living tendencies in the post-COVID-19 era. In terms of “Experience,” people who had bad experiences during the pandemic, for example, poor economic conditions were associated with suburban area l",6.0,
4de8a99bf100e28715f5d6f82774815bd2d6650d,10.37256/rrcs.2320232639,An Analysis of House Price Prediction Using Ensemble Learning Algorithms,2023.0,Research Reports on Computer Science,Sai Venkat Boyapati; M. Karthik; K. Subrahmanyam; B. Reddy,"It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices based on current conditions. From historical data on property markets, literature attempts to draw useful insights. Business trends must be understood so that individuals may prepare their budgetary needs accordingly. A society that is ever-expanding is driven by the growing real estate industry. A lot of clients have been duped by agents setting up a fake market rate. As a result, the real estate industry has become less transparent in recent years. Due to decreased accuracy and overfitting of data, the previous model reduced efficiency, whereas the newly developed model resolves such issues and provides a rich user interface with a better model. An important part of this study is to develop an extensive model that is beneficial to both business societies and individuals. This is the main objective of this study. In order to simplify the client’s fieldwork and free up his time and money, this software is intended to assist him. Machine learning algorithms enable models to be enlightened ",6.0,https://ojs.wiserpub.com/index.php/RRCS/article/download/2639/1395
75f3be3646f879a139ce08cd63ad0e1e3a74ccd5,10.22178/pos.17-13,Determining the Impact of Residential Neighbourhood Crime on Housing Investment Using Logistic Regression,2016.0,,S. Olajide; Mohd Lizam,"This paper discusses the impact of criminal activities on residential property value. With regard to criminal activities, the paper emphasizes on the contribution of each component of property crime. One thousand (1000) sets of structured questionnaire were administered to the residents of residential estates within the South Western States of Nigeria out of which 467 were considered useable after the data screening. Purposive and systematic sampling techniques were used while logistic regression was used to determine the impact of each of the components of residential property crime on housing investment. The results showed the P-Values of 0.000, 0.322, 0.335, 0.545 and 0.992 for violent crime, incivilities and street crime, burglary and theft, vandalism and robbery respectively.  However, the R 2 which represents the generalisation of the impact of neighbourhood crime on housing investment was 44% and aggregate P-value was 0.000. Using the Hosmer and Lemeshow (H-L) test of goodness of fit, the model had approximately 89% predictive probability which is considered excellent. This indicates that the alternative hypothesis is upheld that residential neighbourhood crime is capable of",6.0,https://pathofscience.org/index.php/ps/article/download/262/301
61ab07b06606a74eb148d72446fd2be718e4b01a,10.1016/j.trpro.2021.01.056,Understanding the determinants of spatial-temporal mobility patterns based on multi-source heterogeneous data,2021.0,,Chao Chen; Tao Feng; Meng-Ru Shao; Bao-Zhen Yao,"Abstract With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DAS), it is possible to explore the determinants of urban spatial-temporal mobility patterns using multi-source heterogeneous data. This study aims to use the points-of-interests (POIs) data, house-price data, and floating car data to identify the factors influencing urban mobility in Shanghai. Within a scale of 0.5 km grid, trip production and attraction were stratified according to the traveling intensity, and the critical information related to economy, intermodal connection, land use, and time were also obtained through the multi-source data. The experiment results from an ordinal logistic regression (OLR) analysis show that average house price has a dominating and positive effect on the traveling intensity for both trip production and attraction, followed by land-use factors. However, the effect of scenic spots is found significant only on trip attraction. In addition, shopping is found to insignificantly affect the traveling intensity for both trip production and attraction. Unexpectedly, time factors also have diverse impacts. These findings are expected to help better unders",6.0,https://www.sciencedirect.com/science/article/pii/S2352146521000971/pdf
f32005097eb48ee17eec2f3c4f84376159294837,10.1080/15481603.2023.2232191,Exploring the spatial correlation between accessibility to urban vibrancy centers and housing price from a time-dynamic perspective,2023.0,GIScience &amp; Remote Sensing,Luoan Yang; Baolei Zhang; X. Zhang; Shumin Zhang; L. Yin,"ABSTRACT Urban vibrancy is an important indicator of urban prosperity. Evaluating the correlation between urban vibrancy and housing prices is increasingly necessary. However, most studies have ignored the temporal characteristics of urban vibrancy and only considered its spatial nature. This study optimizes the identification model of urban vibrancy centers and proposes a novel model for evaluating the time-dynamic accessibility of urban vibrancy centers. The model takes into account real-time changes in travel costs and analyses its spatial correlation with housing prices. The case study of Chengdu shows significant variations in the spatiotemporal distribution of urban vibrancy centers. High-vibrancy areas are concentrated near commercial complexes. The time distribution of urban vibrancy accessibility is more concentrated at night (18:00–24:00), and the high-accessibility areas are located around the old city and the newly planned Central Business District in the south. Housing prices and urban vibrancy centers accessibility are mainly negatively correlated spatially. The study emphasizes the temporal nature of urban vibrancy and accessibility and enriches the evaluation model ",6.0,https://www.tandfonline.com/doi/pdf/10.1080/15481603.2023.2232191?needAccess=true&role=button
f8fecd11695987c1e0f29361763d65871a1272eb,10.52465/josre.v2i1.176,Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm,2023.0,Journal of Student Research Exploration,Putri Susi Sundari; Mahardika Khafidz Putra,"In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled ""Ames Housing dataset"" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating ",6.0,https://doi.org/10.52465/josre.v2i1.176
423514ecaa586cf87219f5b1b879de8e9c225cf0,10.3390/buildings14030853,Strategic Design Approaches for Eliciting the Perception of ‘Prestige’ in Housing Consumers,2024.0,Buildings,A. Montañana; M. Nolé; Carmen Llinares,"In the current real estate landscape, there is a growing trend to focus on adding value to products to address the customer’s affective/emotional perspective, his/her perceptions of the ‘Prestige’ of properties being crucial for his/her final assessments of the products. This study delves into the design elements that shape perceptions of ‘Prestige’ using Kansei engineering in 235 participants who evaluated various real estate promotions through: (1) a set of 60 adjectives and (2) reason for purchase (residence or investment). A first factor analysis of the set of adjectives yielded 15 independent axes. A subsequent linear regression indicated that the ‘Prestige’ axis was among the four most important factors in/drivers of property purchase decisions. In the second phase, 62 design elements (and their respective categories) of real estate promotions were identified to establish 16 groups. A linear regression determined that Information presentation and Building were the groups of elements with the greatest impact on subjects’ perceptions of ‘Prestige’. A subsequent Univariate General Linear Model analysis identified the design elements significant for each group, such as Developmen",6.0,https://www.mdpi.com/2075-5309/14/3/853/pdf?version=1711033567
7b6f20aae991c7e86b252b29d3aa5ad32d110c23,10.3846/1648715x.2015.1072856,Identifying determinants of demand for construction using an econometric approach,2015.0,,Heng Jiang; Chunlu Liu,"Economic variation and its effects on construction demand have received a great deal of attention in construction economics studies. An understanding of future trends in demand for construction could influence investment strategies for a variety of parties, including construction developers, suppliers, property investors and financial institutions. This paper derives the determinants of demand for construction in Australia using an econometric approach to identify and evaluate economic indicators that affect construction demand. The forecasting contribution of different determinants of economic indicators and their categories to the demand for construction are further estimated. The results of this empirical study suggest that changes in consumer's expectation, income and production, and demography and labour force are closely correlated with the movement of construction demand; and 14 economic indicators are identified as the determinants for construction demand. It was found that the changes in construction price, national income, size of population, unemployment rate, value or export, household expenditure and interest rates play key roles in explaining future variations in the ",6.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/2133/1725
53c497af7f467497c2bb427abf9122f3fc4e6b7c,10.2139/ssrn.3782326,"Tracking Owners’ Sentiments: Subjective Home Values, Expectations and House Price Dynamics",2020.0,Social Science Research Network,Anthony Lepinteur; S. Waltl,"Economic theory predicts that expectations on future house price growth are related to the current price of a house. We test this relationship for the supply side of the secondary housing market using micro data that links individual expectations to a subjective owner estimated value (OEV). We find a strong causal relationship that optimistic expectations indeed imply higher OEVs as compared to neutral or pessimistic expectations. We find qualitatively and quantitatively consistent results for Italy and the US as well as for booming and gloomy years. Our results survive ample robustness checks. Since we use subjective data on house prices, we first show that OEVs are indeed a valid source to study house price dynamics by performing three types of convergent validity tests. We find that price dynamics derived by either combining OEVs and dwelling characteristics, or making use of repeatedly provided OEVs by the same owner over time reproduce objectively measured market trends strikingly well – even over decades. In contrast, OEVs and objective data tend to differ in levels – potentially due to psychological bias. These results hold for a large set of countries. We hence conclude tha",6.0,https://doi.org/10.2139/ssrn.3782326
84e034ebd3d51ad7a43b9ee7a05bdf9ddcee7564,10.1002/sta4.270,Testing prediction algorithms as null hypotheses: Application to assessing the performance of deep neural networks,2020.0,Stat,David R. Bickel,"Bayesian models use posterior predictive distributions to quantify the uncertainty of their predictions. Similarly, the point predictions of neural networks and other machine learning algorithms may be converted to predictive distributions by various bootstrap methods. The predictive performance of each algorithm can then be assessed by quantifying the performance of its predictive distribution. Previous methods for assessing such performance are relative, indicating whether certain algorithms perform better than others. This paper proposes performance measures that are absolute in the sense that they indicate whether or not an algorithm performs adequately without requiring comparisons with other algorithms. The first proposed performance measure is a predictive p value that generalizes a prior predictive p value with the prior distribution equal to the posterior distribution of previous data. The other proposed performance measures use the generalized predictive p value for each prediction to estimate the proportion of target values that are compatible with the predictive distribution. The new performance measures are illustrated by using them to evaluate the predictive performan",6.0,https://hal.archives-ouvertes.fr/hal-02334579/file/assessment-preprint1911101.pdf
ee96d0e7a91826424bef527f36a42752b589d224,10.1007/s10901-022-09938-0,New housing construction and market signals in urban China: a tale of 35 metropolitan areas,2022.0,Journal of Housing and the Built Environment,Honghao Ren; H. Folmer,"This paper investigates the equilibrium adjustment mechanism of new housing construction in urban China after the 1998 housing market reform. This analysis is based on a panel of 35 metropolitan areas over the period 2001–2015. The new housing supply function is specified in terms of changes rather than levels to capture the disequilibrium state of the Chinese housing market. In addition, current, one-year and two-year lags of the controls are used to capture the impact of the state control of construction land permits (Land Regulation Act). The main outcome is that new housing construction in the metropolitan areas under study responded to market signals but with relatively long time lags. In particular, during the period 2007–2015, new housing construction positively responded to the one-year and two-year lagged changes in housing prices and construction land supply, negatively responded to the current, one-year and two-year lagged changes in the interest rate, and negatively responded to the one-year lagged changes in construction material costs. The main conclusion is that China’s housing marketization has started to work, although it is still subject to its historical footprin",6.0,https://link.springer.com/content/pdf/10.1007/s10901-022-09938-0.pdf
468f24fc8fc96c2ea3fe7721dcab7d11f1adfa20,10.3390/su10040926,"House Prices, Mortgage Rate, and Policy: Megadata Analysis in Taipei",2018.0,,Chien-Ming Yu; Pei-Fen Chen,"There are intensive concerns about the causes of rising housing prices in Taipei. The aims of this study are twofold. The first addresses the issue of whether the low-interest-rate policy is adversely driving housing prices in the metropolitan area of Taipei. The second is to investigate if two important housing policies, luxury tax and actual price registration, help to depress the rising residential house prices. With the mega tick data of Taipei city for more than 80,000 residential house transaction records, we examined the factors influencing the actual house prices spanning the period from June 2008 through May 2014. We applied the least squares regression and the quantile regression in the model estimations for housing valuation. In addition, the megadata set is organized in time series and cross-section structures for five subdistricts and the whole Taipei city as well. The empirical results show that low mortgage rates have been the most significant factor for soaring housing prices in Taipei for the past decade. We estimate that a 1% increase in mortgage rates reduces housing prices from 5% to 17%. The actual price registration policy contributes to the decrease in housin",6.0,https://www.mdpi.com/2071-1050/10/4/926/pdf?version=1525344816
8bbe530fffd86e858488ca4a04253ad4e0a6b2e4,10.21511/ppm.20(3).2022.46,Applied aspects of time series models for predicting residential property prices in Bulgaria,2022.0,Problems and Perspectives in Management,Svetoslav Iliychovski; Teodora Filipova; M. Petrova,"Accurate housing price forecasts play a critical role in balancing supply and demand in the residential real estate market, as well as in achieving the goals of various stakeholders – buyers, investors, construction contractors, public administration, real estate agencies, special investment purpose companies, etc. The present study aims to investigate the relationship between specific predictors and build a suitable model for forecasting housing prices in Bulgaria. In this regard, a study was conducted on transactions with residential real estate in the city of Sofia for the period from the first quarter of 2016 to the fourth quarter of 2021. The ARIMA model is used in the development to predict the values of the variables. Eight models are tested for the researched factors (24 in total). On this basis, the price per square meter of residential property was predicted, including estimated values from the ARIMA model for the parameters involved in the regression equation. The result showed that there is a strong relationship between the analyzed predictors and the studied variable – price per square meter of housing. The tested models are adequate and the statistical requirements fo",6.0,https://doi.org/10.21511/ppm.20(3).2022.46
1cbdf7f0f997ee3da2127dbb3c2e026c16ec662e,10.1007/s41060-025-00862-4,Uncertainty quantification in automated valuation models with spatially weighted conformal prediction,2023.0,International Journal of Data Science and Analysis,A. Hjort; G. Hermansen; Johan Pensar; Jonathan P. Williams,"Nonparametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but a main drawback of such methods is their limited ability to quantify prediction uncertainty. Conformal prediction (CP) is a model-agnostic framework for constructing confidence sets around predictions of machine learning models with minimal assumptions. However, due to the spatial dependencies observed in house prices, direct application of CP leads to confidence sets that are not calibrated everywhere, i.e., the confidence sets will be too large in certain geographical regions and too small in others. We survey various approaches to adjust the CP confidence set to account for this and demonstrate their performance on a data set from the housing market in Oslo, Norway. Our findings indicate that calibrating the confidence sets on a spatially weighted version of the non-conformity scores makes the coverage more consistently calibrated across geographical regions. We also perform a simulation study on synthetically generated sale prices to empirically explore the performance of CP on housing market data under idea",6.0,https://link.springer.com/content/pdf/10.1007/s41060-025-00862-4.pdf
fbe8548c89ee46ca6468c1cc9e57c0c5a6af1a39,10.22214/ijraset.2019.5251,House Price Prediction Analysis using Machine Learning,2019.0,International Journal for Research in Applied Science and Engineering Technology,S. B.,": Land is the least straightforward industry in our biological system. Lodging costs continue changing all day every day and now and then are advertised instead of being founded on valuation. Anticipating lodging costs with genuine elements is the principle essence of our exploration venture. Here we plan to make our assessments dependent on each fundamental parameter that is considered while deciding the cost. We utilize different relapse systems in this pathway, and our outcomes are not sole assurance of one method rather it is the weighted mean of different procedures to give most exact outcomes. The outcomes demonstrated that this methodology yields least blunder and most extreme precision than individual calculations connected. We additionally propose to utilize ongoing neighbourhood subtleties utilizing Google maps to get accurate genuine valuations. House costs increment consistently, so there is a requirement for a framework to anticipate house costs later on. House value expectation can enable the engineer to decide the selling cost of a house and can assist the client with arranging the correct time to buy a house. There are three factors that impact the cost of a house w",6.0,https://doi.org/10.22214/ijraset.2019.5251
baaec29e29c5f374e6501b179a01b6732b900c04,10.2991/aebmr.k.220307.253,House Price Prediction Based on Machine Learning: A Case of King County,2022.0,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Yijia Wang; Q. Zhao,"This paper focuses on formulating a feasible method for house price prediction. A dataset containing features and house price of King County in the US is used. During the data preprocessing, extreme values are winsorized and highly correlated features are removed. Eight models including Catboost, lightGBM and XGBoost serve as candidate models. They are evaluated by several evaluation indicators, including rooted mean square error, R-squared score, adjusted R-squared score and K-fold cross validation score. The model that has low RMSE, achieves a high R-squared score and adjusted R-squared score, especially in the test set, and acquires a high score in cross validation is considered a better model. This paper finds out that Catboost performs the best among all models and can be used for house price prediction. Location, living space and condition of the house are the most important features influencing house price. After comparison and contrast with other papers, it is attested that findings in this paper conform to real life. This paper formulates a model that fits better than preceding studies for house price prediction and makes necessary supplement to the exploration of features",6.0,https://www.atlantis-press.com/article/125971834.pdf
e9e73ead1d60e4bd020d4ec92dbe95eebfe0762d,10.1371/journal.pone.0307947,A study on the impact of high-speed rail on the consumption structure of urban and rural residents in China,2024.0,PLoS ONE,Ya-Fei Xu; Guoli Ou,"Consumption is crucial to individual well-being and national economic development. This study investigates whether high-speed rail (HSR) influences consumption expenditure (CE) and consumption structure (CS) of urban and rural Chinese citizens. Using panel data from 2003 to 2019 and econometric models, this study finds that: (1) HSR significantly increases CE for both urban and rural residents, promotes CS upgrades in rural areas, but inhibits CS upgrades in urban areas. These results remain robust after extensive testing. (2) HSR’s impact on urban consumption is relatively focused and singular, whereas its effect on rural consumption is dispersed and extensive. Additionally, the impact of HSR on consumption exhibited significant delays and regional characteristics. (3) Mediation analysis reveals that HSR significantly enhances urban and rural CE and facilitates rural CS upgrades through market, price, and income effects. However, it also triggers housing price increases, impeding urban CS upgrades. This study provides important references for the government to optimize transportation infrastructure investments, promote balanced economic development between urban and rural areas, a",6.0,https://doi.org/10.1371/journal.pone.0307947
50b770be3980d88d8f40641c8ff78c06bf2317b1,10.1142/s2737599425500355,An empirical investigation of Lijiang’s residential property valuation in China using an integrated framework with Gaussian process regression and Bayesian hyperparameter tuning,2025.0,Innovation and Emerging Technologies,Bing-Zi Jin; Xiao-Jie Xu,"Over the last decade, China’s property market has undergone pronounced cyclical shifts, transitioning from sustained growth to a marked slowdown following macroeconomic shocks in late 2021. These structural adjustments—shaped by evolving regulatory interventions and growing fiscal uncertainty—have posed complex forecasting challenges for stakeholders such as institutional investors, governmental regulators, and urban development planners charged with anticipating metropolitan housing value trends. To address the shortcomings of traditional prediction techniques, this research introduces an advanced framework based on Gaussian process regression, enhanced by adaptable kernel configurations and tailored feature engineering underpinned by domain expertise. The framework analyzes the temporal evolution of housing prices in Lijiang, Yunnan Province, utilizing a 9-year panel dataset spanning October 2015 to July 2024. By integrating Bayesian hyperparameter tuning with nested cross-validation, the model achieved substantial out-of-sample accuracy gains for October 2022 to July 2024, registering a relative root mean square error of 0.1209%. These results confirm the method’s ability to cap",6.0,
4aa8caff5f11a1c2986cddd8bd1ba6e3e457c42b,10.22452/mjes.vol54no2.8,Can the Housing Policies Sustain House Prices in Malaysia,2017.0,,Geok Peng Yeap; H. Lean,"In addition to economic factors, this paper examines the impact of housing policies in sustaining house prices in Malaysia at both aggregate and disaggregate levels. By using autoregressive distributed lag (ARDL) framework, we develop a model of house price determination with a focus on two major housing policies–mortgage interest tax relief (MITR) and Developer Interest Bearing Scheme (DIBS). Although aggregate house prices do not react to any economic variable in the long run, it responds positively to interest rate and DIBS in the short run. At the disaggregate level, population and housing stock influence house prices in the long run. Nonetheless, MITR and DIBS have a statistically significant positive impact on the price of high-rise houses. We conclude that while demand and supply variables are important determinants of house prices in the long run, house price changes are affected by housing policies in the short run. For policy recommendation, we suggest to strictly prohibit DIBS to ensure a stable growth in the housing market. Besides that, MITR should be implemented to ensure housing affordability and to stimulate the housing market during recession.",6.0,https://mjes.um.edu.my/article/download/8634/5999
d4234858d553d0f42b37b21d49090126382e1733,10.1108/ijhma-08-2022-0114,The impact of immigration on urban housing prices in Indonesia,2023.0,International Journal of Housing Markets and Analysis,Farida Nurkhayati; A. Fitrady,"
Purpose
Rural–urban migration has led to an increase in the community’s need for housing in the migration area. The demand for housing is getting higher while the land availability does not increase so that house prices will continue to increase. This study aims to estimate the impact of immigration on urban housing prices in Indonesia.


Design/methodology/approach
This study examines the effect of immigration on urban housing prices at the city level in Indonesia by using 14 major cities data from 2012 to 2020 to build a panel data model. The model also incorporates urban economic conditions as control variables.


Findings
From the national level, the authors find that inter-regional migration has a significant and positive impact on urban housing prices. Based on the results, this paper suggested addressing the volatility of house prices through the provision of decent and affordable housing improvement to meet the growing needs and demands of the immigrant population.


Research limitations/implications
This study still has several limitations: the sample of cities used is not comprehensive enough, and the time period used is not long enough; the spatial impact on house price",6.0,
ad81ba69861b213822b59cdbe3a85fbee38aa116,10.3390/su142114591,Research on Sustainability of Financing Mode and Demand of PPP Project—Based on Chinese PPP and Local Financing Platform Alternative Perspective,2022.0,Sustainability,Fu-Sheng Xie,"This paper studies the sustainability of the financing model in China’s urbanization and the demand of local governments for PPP projects. Based on the integrated panel data of PPP, local investment and financing platforms, urban investment bonds, and local economic statistics, the fixed effect model and dynamic panel regression model are used to study whether local financing platforms promote economic growth. The results show that in general, the development model of financing platform is not conducive to sustainable economic development. Before the 2008 economic crisis, local governments were pushing up house prices through financing platforms which boosted economic growth, but after the 2008 economic crisis, this mechanism did not work. Therefore, the sample selection model is used to predict the demand of local PPP projects and verify the substitution relationship between local financing platforms and PPP. The study found that financing platforms hinder local government demand for PPP projects and the attraction to private investment. After adjusting the relevant variables to zero, the demand for PPP projects in a representative city is 3.46.",6.0,https://www.mdpi.com/2071-1050/14/21/14591/pdf?version=1668065535
ceddd47683104acf14b3b3363d0843983e7a79cd,10.1111/ajes.12386,Revisiting the Concept of the Property State: Private Landowners and Suburban Development in Hong Kong,2021.0,The American journal of economics and sociology,Y. Yau; Choi-Cheung Tin,"Under the prevailing land‐leasehold system, the Hong Kong government is thought to be the dominant supplier of land for housing development and for economic transformation. These characteristics make Hong Kong a “property state,” to use a concept developed by Anne Haila. However, the protection of private property rights has long been a core value in the city. As a result, private developers and investors have endeavored to build up their own land banks, enabling private‐profit‐oriented entities to dominate the land supply market for housing development. This article supplements previous works on land monopoly in Hong Kong. While previous works focused mainly on the role of the property lobby in the disposal of new land by the government, this article offers another perspective on land monopoly, based on the case of underutilized land resources in the New Territories of Hong Kong. By focusing on growth coalitions and the projects that they introduce to boost growth and enhance land values, this article provides insight into the political economy of urban development in Hong Kong. In light of the soaring property prices in the city, the Hong Kong government planned to open up the su",6.0,
c968aee072b3778a6e8832f9bbd33398e466d511,10.59139/ws.2013.05.3,Zastosowanie modeli zmiennych jakościowych do badania ceny rynkowej mieszkań,2013.0,Wiadomości Statystyczne. The Polish Statistician,B. Batóg; I. Foryś,"The article presents the results of the housing market survey in Warsaw in 2009 and 2010. Offers were described by a set of several quantitative and qualitative variables, including the transaction price. In the studied years operated the ”Rodzina na swoim” (”Family on its own”) program, in which the housing price limits were introduced on the opportunity to apply for funding. However, the limits of budget expenditure resulted in the introduction of savings and announcement of the program realization in a modified formula. In this context, conducted by the authors the secondary housing market research shows the characteristics of a dwelling eligible for purchase by the state fund. The co-financing threshold in Warsaw was treated as a dichotomous variable (a feature which is included or not in the range of co-financing). Binomial logit models were used for so defined dependent variable. As independent variables were used both quantitative and qualitative housing characteristics. The use of this type of models allows to assess which variables have the greatest impact on the price of meeting the criteria of the State housing assistance program.",6.0,http://ws.stat.gov.pl/WS/2013/5/WS_05_2013__3_Barbara BATÓG Iwona FORYŚ — Zastosowanie modeli zmiennych jakościowych do badania ceny ry.pdf
59256c0a62b571fd4deb6268c8fa9ca7a96266fe,10.1080/00167428.2023.2217266,"RENT-BURDENED IN THE SOUTH? A NEIGHBORHOOD-SCALE ANALYSIS OF DIVERSITY AND IMMIGRANTS IN NASHVILLE, TENNESSEE",2023.0,Geografická revue,Madhuri Sharma; M. Samarin,"ABSTRACT Rental affordability has become a major concern for most Americans since the 2007–2009 recession. Minorities and immigrants, especially in some emerging destination metropolises, have suffered disproportionally from its brunt. Nashville—a prominent southern destination—has not only experienced a considerable gain in diverse immigrant groups, but it has also witnessed skyrocketing housing prices and rents, making them beyond the affordability of even middle-class Americans. Rent burden patterns among racial/ethnic groups, especially the immigrants/foreign-born who are not yet citizens, remain largely unknown. Given Nashville’s suitability as an emerging metropolis reverberating as a nationally representative immigrant gateway, this article explores relationships between rent burden faced by immigrants and other racial/ethnic groups while examining neighborhood-level determinants captured in principal components derived from demographic, socioeconomic, occupation, and built-environment attributes. A newly devised rent burden index—previously applied for metropolises only—is calibrated to census tracts to test its validity in explaining intraurban variabilities. Through multi",6.0,
f94619c229e58b9b862cc414623b81162426ad7b,10.3390/su14010352,Full Lead Service Line Replacement: A Case Study of Equity in Environmental Remediation,2021.0,Sustainability,Karen J. Baehler; M. McGraw; Michele J. Aquino; R. Heslin; Lindsay McCormick; T. Neltner,"In the U.S., approximately 9.3 million lead service lines (LSLs) account for most lead contamination of drinking water. As the commitment to replace LSLs with safer materials grows, empirical evidence is needed to understand which households are benefitting most from current replacement practices. This exploratory study analyzes factors predictive of whether an LSL was replaced fully (from water main to premise) or partially (only the portion on public property). Conventional ordinary least squares, negative binomial, and geographically weighted regression models are used to test the hypothesis that full lead service line replacements (LSLRs) were less common in lower-income, higher-minority neighborhoods under a cost-sharing program design in Washington, D.C. between 2009 and 2018. The study finds supportive evidence that household income is a major predictor of full replacement prevalence, with race also showing significance in some analyses. These findings highlight the need for further research into patterns of full versus partial LSLR across the U.S. and may inform future decisions about LSLR policy and program design.",6.0,https://www.mdpi.com/2071-1050/14/1/352/pdf?version=1640779556
fbc07523e1163aba60b0610914815fae3e0fbe46,10.1108/pm-01-2021-0003,Quantifying the value of high-quality property management,2021.0,Property Management,Jayantha Wadu Mesthrige,"Purpose“Ill-maintenance of common areas” in multi-owner residential properties is described as the “tragedy of anti-commons problem”. The problem can be addressed by outsourcing the management of the property to a company possessing quality property management (PMGT) certification. The PMGT certification is normally hailed as an effective means of reassuring occupiers and prospective property buyers of the good level of quality of the property management to be expected. This study investigated whether PMGT certification carries with it a price premium for residential properties.Design/methodology/approachA fixed-effects modelling approach was employed in relation to a large residential properties dataset in Hong Kong, in the period 2009–2018.FindingsThe results indicate, on average, that the PMGT certification commands a price premium of between 3.3 and 3.9%. This premium can be called, the “tragedy of anti-commons premium (TAC premium)”. The results further suggest that significant price premium differences relate to the four different types of certifications studied. The price of a residential unit managed by a property management company (PMC) with Q-mark certification is about ",6.0,
661a4535483a58845f8bd5f220c63a9bc4075e58,10.1080/1351847x.2019.1588763,Anticipating critical transitions of the housing market: new evidence from China,2019.0,European Journal of Finance,Qun-Zhi Zhang; D. Sornette; Hao Zhang,"ABSTRACT We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's metropolises. Building on the Log-Periodic Power Law Singularity (LPPLS) model of self-reinforcing feedback loops, we use the quantile regression calibration approach recently introduced by two of us to build confidence intervals and explore possible distinct scenarios. We propose to consolidate the quantile regressions into the arithmetic average of the quantile-based LPPLS Confidence indicator, which accounts for the robustness of the calibration with respect to bootstrapped residuals. We make three main contributions to the literature of real estate bubbles. First, we verify the validity of the arithmetic average of the quantile-based LPPLS Confidence indicator by studying the critical times of historical housing price bubbles in the U.S., Hong Kong, U.K. and Canada. Second, the LPPLS detection methods are applied to provide early warning signals of the housing markets in some metropolises in China. Third, we determine the possible turning points of the markets in Beijing, Shanghai, Shenzhen, Guangzhou, Tianji",6.0,
d9076a1590ae075b8ad8d8be3b1df7d65bd269d6,10.1177/21582440221079916,How on Earth Did Spanish Banking Sell the Housing Stock?,2022.0,SAGE Open,José Torres-Pruñonosa; Pablo García-Estévez; J. Raya; Camilo Prado-Román,"The accumulation of properties by Spanish banks during the crisis of the first decade of the 21st century has definitely changed the housing market. An optimal house price valuation is useful to determine the bank’s actual financial situation. Furthermore, properties valued according to the market can be sold in a shorter span of time and at a better price. Using a sample of 24,781 properties and a simulation exercise, we aim to identify the decision criteria that Spanish banking used to decide which properties were going to be sold and at what price. The results of the comparison among four methods used to value real estate—artificial neural networks, semi log regressions, a combined model by means of weighted least squares regression, and quantile regressions—and the actual situation suggest that banking aimed to maximize the reversal of impairment losses, although this would mean capital losses, selling less properties, and decreasing their revenues. Therefore, the actual combined result was very detrimental to banking and, consequently, to the Spanish society because of its banking bailout.",6.0,https://journals.sagepub.com/doi/pdf/10.1177/21582440221079916
c72939e9d4a74a8e2bb4f7f31840b58b6fe8f82f,10.4236/oalib.1107118,Study on the Spatial Differentiation Pattern of Housing Prices in Chengdu-Chongqing City Cluster,2021.0,OALib,Ren-Kang Feng; Rubing Han,"Taking the housing prices of 151 districts and counties in the Chengdu-Chongqing urban agglomeration from 2017 to 2019 as the basic data, the housing price data is geospatially expressed in ArcGIS, and the Theil index is calculated to analyze the differentiation pattern of the housing prices of the Chengdu-Chongqing urban agglomeration, using geo-detector. The effect of different factors on housing prices in different regions shows that the geographical distribution of housing prices in the Chengdu-Chongqing urban agglomeration is basically the same as the city scale and economic development of each city. The housing prices of the Chengdu-Chongqing urban agglomeration show a dual-core dominance and a collapse in the central region. The characteristics of industrial structure, housing supply, population scale, land cost, economic strength, real estate investment scale, housing policy, and higher education resources have a significant impact on the housing prices of Chengdu-Chongqing urban agglomeration. Different indicators have different effects on housing prices in different cities.",6.0,https://doi.org/10.4236/oalib.1107118
b325828e23e167c2be02c54492ef18c06f07cb9f,10.1109/dese58274.2023.10099796,A Novel Predictive Model for Housing Loan Default using Feature Generation and Explainable AI,2023.0,International Conference on Developments in eSystems Engineering,M. Mahyoub; Shatha Ghareeb; Jamila Mustafina,"Home Loan plays a pivotal role in today's age when one steps into purchasing their home. It has been witnessed that in many cases users are unable to pay the after taking the loan and thus the loan is slipped to NPA(Non-Performing Asset) from Standard Asset for the bank or any lending institution. The revenue generation is ceased. As the housing loan is taken against property the lenders have right to sell the property and close the dues, but the process is lengthy as judicial procedures are involved. In most cases, the property value is much less than the calculated loan amount (Principal + Interest). In this study we examined the several ML methods to identify the loan default before disbursing the loan to the applicant. This matter has been studied widely and used the predictive analytics to find out the relationship between attributes and the target variable. Predictive Analytics enables us to feed optimal set of features to the ML models. The study started with 122 attributes and ended up with around 30% of features as the ideal subset for housing loan default prediction. Then, five ML models were fit into the dataset and the champion model came up with roc score 0.94, Recall ",6.0,
9b2d45736a7733783d51884fa5196ebdf1499baf,10.5334/bc.298,"Housing, street and health: a new systemic research framework",2023.0,Buildings and Cities,Anna Pagani; D. Christie; Valentin Bourdon; C. W. Gago; S. Joost; Dusan Licina; Mathias Lerch; C. Rozenblat; I. Guessous; Paola Viganó,"As the world’s population grows in cities, urban dwellers spend a large amount of time inside their home, making housing health ever more important. Critical for residents’ health, the interactions between indoor residential environments and outdoor environmental conditions ( e.g. air pollution, noise, heat) are mediated by the controversial and evolving relationship between housing and the street. Currently, there is a lack of ways to integrate and explore synergies among the plurality of perspectives that have addressed the interactions between housing, street and health (HSH). This paper proposes a systemic research framework to address conceptually, spatially and temporally HSH interactions. With a focus on European cities, determinants of housing health are identified through six perspectives, comprising environmental health, domestic architecture, building technologies, socio-economic inequalities, housing prices and urban planning. Their interrelationships are organised in a causal loop diagram, which can be used to highlight gaps in",6.0,https://storage.googleapis.com/jnl-up-j-bc-files/journals/1/articles/298/64e3576c3f375.pdf
b1dfd94d1679356da4745f38b2021b83df3dc11b,10.1590/s0101-41612010000400006,Um modelo espacial de demanda habitacional para a cidade do Recife,2010.0,,Rubens Alves Dantas; A. Magalhães; José Raimundo de Oliveira Vergolino,"This paper shows the importance of using spatial econometrics in the studies related to the analysis of housing market's behavior. In empirical analysis carried out on the object of estimating a Housing Demand Function for the city of Recife, Brazil, based in information from the Demographic Census (2000) and data from real state financed by Caixa Economica Federal, strong indications of spatial dependency were verified in all explored economical variables. One verifies that the manner of considering the spatial issue as a function of distances of housing to influence poles or space division in regions, like currently occurring in literature, is not capable of explaining entirely the behavior of the housing demand. The results indicate that the demand equations traditionally estimated, without considering the effects of spatial dependency may generate biased results, where the price-elasticity by the traditional model represents less than 50% of the estimates of the spatial model.",6.0,https://www.scielo.br/j/ee/a/jYc7ZzSMWV6Qtjx3nstjvMh/?lang=pt&format=pdf
96e0ec640b350c1ec477b4a65c34f99c77d53503,10.2478/v10135-011-0004-0,Identifying Determinants of Residential Property Values in South London,2011.0,,D. E. May; Adam R. Corbin; Philip D. Hollins,"Identifying Determinants of Residential Property Values in South London This paper advances the academic work conducted on identifying some of the determinants of residential property values in different countries. Such determinants might include positive factors such as access to amenities, or negative factors such as high voltage overhead transmission lines (HVOTL). The objective explored here is to extend this research in the UK by considering the particular case of residential property values in South London. A number of determinants is considered, grouped and a model which explains the effect of the determinants on value is produced. The elasticities between property values and the distances from different determinants were also analysed to help highlight relationships between the determinants and residential property values. The results showed a relationship between residential property values and the determinants, and a relationship between the determinants themselves.",6.0,https://sciendo.com/pdf/10.2478/v10135-011-0004-0
58563c3a6449027ecf641c7726d34d6cc91d2a93,10.1080/00330124.2020.1812408,Predicting Model Improvement by Accounting for Spatial Autocorrelation: A Socioeconomic Perspective,2020.0,,Daehyun Kim; Insang Song,"In geographical literature, numerous studies have demonstrated the differences that arise if spatial autocorrelation (SAC) is incorporated into a conventional nonspatial modeling procedure, but little is known about when these differences might be magnified. This study addressed this query by conducting two sets of regression modeling for 561 variables representing housing prices, metropolitan industry, health, crime, education, and (un)employment across various parts of the United States: (1) nonspatial ordinary least squares (OLS) using a set of selected independent variables and (2) spatial regression incorporating spatial filters into the nonspatial OLS as additional independent variables. This incorporation generally improved the model outcomes through decreases in residual autocorrelation and Akaike’s information criterion (AIC). The degree of improvement correlated positively with the level of SAC inherent in the dependent variables. That is, strongly autocorrelated socioeconomic variables underwent greater decreases in residual autocorrelation and AIC than those variables with weaker SAC. The results imply that spatial modeling outcomes are sensitive to and potentially pred",6.0,
457f8b25f789f1bb3e25a9824862ae1aaca4c2f4,10.4067/s0718-88702016000200005,International Synchronicity of Housing Prices,2016.0,,A. Jara; N. Romero,"While the synchronization of business cycles has been much studied in the literature, housing prices’ synchronization has not. In this paper we study the determinants of the housing prices’ synchronicity for a variety of advanced and emerging countries. In order to do so, we rely on Kalemli-Ozcan et al. (2009)’s definition of synchronicity and on the housing prices indexes provided by the Bank for International Settlements (BIS) and other sources. In particular, we study the relationship of the housing prices’ synchronicity and banking integration. Our finding show that, after controlling by macroeconomic and idiosyncratic factors, banking integration positively and significantly affects the synchronicity of housing prices. We also find that this effect is lost during the global financial crisis for the synchronicity between advanced countries, but it remains significant for the synchronicity between advanced and emerging economies.",6.0,https://scielo.conicyt.cl/pdf/rae/v31n2/art05.pdf
9ff09c1e37da9c1a4280e01b0a0249aa8f1cd17d,10.33736/uraf.1215.2018,Empirical Analysis of Factors Influencing Residential Property Prices in Malaysia,2018.0,UNIMAS Review of Accounting and Finance,Nazaria Binti Md. Aris,"This study concerns the factors influencing the prices for residential properties in Malaysia as well as their relationship towards residential property prices. The data collected and analysed in this research is from quarter one year 2000 to quarter four year 2015. Various determinants have been identified namely country population, Gross Domestic Product, household income, inflation and lending rates in this research. The time-series analysis methodologies adopted in this research are the Augmented Dickey-Fuller (ADF) and Philip-Perron (PP) test for unit root, Johansen and Juselius Cointegration Test, Granger Causality Test for Vector Error Correction Model (VECM) and also Variance Decomposition (VDC).  In this study, these two variables, population growth (POPGROWTH) and inflation measured by the consumer price index (CPI) were found has a significant and positive effect towards the price of residential properties in Malaysia.",6.0,https://doi.org/10.33736/uraf.1215.2018
65b8dc51a260078bf728b6a726a0e791cb09629d,10.1088/1755-1315/612/1/012041,"Willingness to pay for flood insurance: a case study in Phang Khon, Sakon Nakhon Province, Thailand",2020.0,IOP Conference Series: Earth and Environment,S. Paopid; J. Tang; N. Leelawat,"Long droughts and floods can reduce agricultural productivity and negatively impact upon businesses in the agriculture and food industries. The prevention and response to floods require both public and private efforts (e.g., “Pay for Flood” insurance) for utmost effectiveness. The purpose of this study was to determine the potential factors that influence the people of Phang Khon District, near the Nam Un Dam, in paying flood insurance. The regression results showed that the height and duration of flooding, house price, and flood damage, were all key factors that triggered the willingness to pay flood insurance. In contrast, having a house with stairs decreased the likelihood of paying insurance. Our research suggested that 12.69 -19.04 USD/month is a commonly accepted flood insurance premium in Thailand. These findings can guide governments and insurance providers and companies towards improving their flood insurance practices.",6.0,https://iopscience.iop.org/article/10.1088/1755-1315/612/1/012041/pdf
32c1cd47836c2100e79770f6bce215bd48903e96,10.1177/0308518x18811671,The spatial consequences of the housing affordability crisis in England,2018.0,Environment and Planning,Nikodem Szumilo,"This paper discusses the impact of housing affordability on the spatial distribution of productivity and wages. The key theoretical contribution is to phrase the problem as an issue of the composition (rather than the level) of housing demand and link it to heterogeneous preferences and characteristics of households. Using a simple simulation methodology, the study estimates levels of amenity values and wages that would make current house prices as affordable as they were in 1995 in all English local authority districts. Although average wages would be unlikely to increase if housing was more affordable, productivity across England would probably be higher as the spatial distribution of economic activity would change. The key conclusions are that (1) unaffordable housing has significant economic implications; and (2) policy aimed at improving housing affordability should consider targeting housing demand as well as supply.",6.0,https://researchonline.lse.ac.uk/id/eprint/100015/1/Szumilo_spatial_consequences_of_the_housing.pdf
9e7ab4a4550ac76b2e863a6f5ac4e84d53b113d4,10.1088/1742-6596/1629/1/012071,Quantitative study on factors affecting the price of residential real estate multiple linear regression model,2020.0,"Journal of Physics, Conference Series",Fa-Qiang Cui,"To study the impact of the “zero threshold” household entry policy on the fluctuation of residential commodity housing prices in Jinan, this paper uses the average sales price data of residential commodity housing in Jinan from 2006 to 2018, selects 6 relevant indicators including the total population at the end of the year, and constructs a multiple linear regression. The model uses Eviews7.2 software, innovatively implies variables such as equivalent sound level, performs stepwise regression analysis, weighted correction regression model correction, and performs statistical tests such as heteroscedasticity and sequence correlation. The results show that the local general public budget expenditure and the average equivalent sound level of roads and environment have a positive impact on prices, while the local general public budget revenue and the total population at the end of the year have a negative impact on prices.",6.0,https://iopscience.iop.org/article/10.1088/1742-6596/1629/1/012071/pdf
cd316c303cb1024fdf65f51f5d8fbe9b673944b4,10.21837/pm.v18i13.797,"DO INFLATION, INTEREST RATE AND COST OF RENTING AFFECT THE PRICE OF TERRACE HOUSES IN PENANG?",2020.0,PLANNING MALAYSIA,Zaemah Zainuddin; Rosylin Mohd. Yusof,"In Malaysia, the housing ownership is reported to decrease from 85% in 1999 to 72.5% in 2010. This is due to the outstripped increase of house price over the income level and the unstable economic situation which creates unaffordability to own a house for many people. Therefore, the main objective of this study is to examine whether the price of terrace houses in Penang is being affected with fundamental factors such as inflation, interest rates and the cost of renting. This study uses multivariate regression analysis with quarterly data of terrace house prices (HPI terrace house in Penang), inflation (CPI) and interest rate (mortgage rates) from 2009: Q1 to 2016: Q4. Evidently, the cost of renting terrace houses in Penang does not have any impact on the price of terrace houses and the stable movement of cost of renting indicates that the growth of rental rate is at acceptable price for middle income earners.",6.0,https://www.planningmalaysia.org/index.php/pmj/article/download/797/598
bc005e140deb2394c7a5dfba0f2bb0648f2f8f13,10.1016/j.ijforecast.2021.06.007,Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation,2021.0,International Journal of Forecasting,Rashad Ahmed; M. Pesaran,"This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000-2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex post evaluation of the forecasts released in real time prior to the election. It is shown that all of these forecasts outperform autoregressive benchmarks, with a pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperforming all models in forecasting both the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international comp",6.0,
a74dcde3d3f15d8253b6e915046fae427ae91082,10.5897/ajbm11.1626,"How to affect housing values: Location, affordability and amenity",2012.0,,M. Guan,"During the last ten years, real house prices have increased in China at a rate faster than inflation and job compensation. It is now a conventional wisdom that location, affordability and amenity increase housing values in the neighborhoods and communities they inhabit. But, few existing studies deal with it. This paper aims to report on a study that contributes to the understanding of the roles of location, affordability and amenity on housing values in China. The key findings confirm the general theory and hypotheses. The analysis presents evidence that location, affordability and amenity are positively influenced by housing values. The correlation among location, affordability and amenity are also computed. This research contributes to the understanding of the determinants of housing values in emerging markets like China. 
 
   
 
 Key words: Housing, affordability, location, amenity.",6.0,https://doi.org/10.5897/ajbm11.1626
354284b564059f8232fd974f92ea9958c463f7fd,10.48550/arxiv.2510.13774,UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations,2025.0,arXiv.org,Dominik J. Mühlematter; Che Lin; Ye Hong; M. Raubal; Nina Wiedemann,"Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize task-specific models, while recent generic models for spatial representations often support only limited modalities and lack multimodal fusion capabilities. To overcome these challenges, we present UrbanFusion, a spatial representation model that features Stochastic Multimodal Fusion (SMF). The framework employs modality-specific encoders to process different types of inputs, including street view imagery, remote sensing data, cartographic maps, and points of interest (POIs) data. These multimodal inputs are integrated via a Transformer-based fusion module that learns unified representations. An extensive evaluation across 41 tasks in 56 cities worldwide demonstrates UrbanFusion's strong generalization and predictive performance compared to state-of-the-art GeoAI models. Specifically, it 1) outperforms prior models on location-encoding, 2) allows multimodal input during inference, and 3) generalizes well to regions unseen during training. UrbanFusion can flexibly utilize any subset of available modalities fo",6.0,
445898473dc375739ebc1a3f2eca542de5062854,10.46501/ijmtst061220,A Comparative Study on House Price Prediction,2020.0,,Akash Kapoor,"Machine learning plays a major role from past years in image detection, spam reorganization, normal speech
command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in
enhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase in
urbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, to
determine a more effective way to calculate house price accurately is the need of the hour. So, an effort has
been made to determine the most accurate way of predicting house price by using machine learning
algorithms: Multivariable Linear Regression, Decision Tree Regression and Random Forest Regression and it
is determined that Multivariable Linear Regression has showed most accuracy and less error.",6.0,https://doi.org/10.46501/ijmtst061220
a4a30b6a1583e401dba9ca9bbe79745cd4fdfa7a,10.1111/1540-6229.12471,ADUs in Los Angeles: Where are they located and by how much do they raise property value?,2024.0,Real Estate Economics,J. Brueckner; S. Thomaz,"Using data from Los Angeles, this article explores the locational determinants as well as the assessed‐value effects of the presence of accessory dwelling units (ADUs). The results show that ADUs are less likely to be found on large parcels containing newer houses and at dense locations near the CBD, the LAX airport, and beaches. ADU presence is more likely close to commercial districts, light‐rail stations, and educational establishments but less likely in higher income areas and Black neighborhoods, although parcels in Latino neighborhoods are more likely to contain ADUs. The assessed‐value regressions show that ADU presence raises a parcel's assessed value and selling price by 7%–9%, while also accurately capturing the unusual rules for property assessments under California's Proposition 13.",6.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-6229.12471
77f3a1deb2c3b9e8703548840034d0920ab52994,10.1093/jeg/lbad034,Are local retail services an amenity or a nuisance?,2023.0,Journal of Economic Geography,Pooya Ghorbani; Rachel Meltzer,"
 We test whether local retail services are considered a nuisance or an amenity and how this distinction is capitalized into residential property values. Using a rich, micro-spatial dataset on property sales transactions and business activity in New York City, we estimate the impact of access to neighborhood retail services on residential sales prices. We construct two instruments to channel supply-side drivers of retail change and to address concerns of endogeneity between changes in retail activity and property values. Results show that retail services that are more frequently consumed and experiential, and are located in relatively more mixed-use neighborhoods are positively capitalized into property values. Residents also pay more to be closer to more diverse retail clusters, and relatively less to be closer to chains. This is true across smaller 1-to-4 family homes as well as larger condos/coops and multi-family rental buildings. The price effects from certain classifications of retail, like restaurants and personal services, are mixed depending on the kind of residential property and the local concentration of the retail. Therefore, the relative strength of the amenity or nui",6.0,
2b1d3717efdab1f6e8e9081464737bc38060da5d,10.1109/ic3i59117.2023.10398134,Real Estate Forecasting System Using Machine Learning Algorithms,2023.0,International Conferences on Contemporary Computing and Informatics,Harshpreet Kaur; Kushwanth A; S. M; Akshay Y; K. Y; S. B.; Ganesh Kamal Reddy G,"The idea of this paper is to predict the value of houses and properties of the market value. The aim of the presented paper is to estimate the market value of the asset being sold. This application assists in determining a location's beginning pricing based on location-specific factors. The below mentioned idea to find the price of a location based on location variables. The below discussed models helps to find the market value of any property in India through different databases depending upon the locations. The presented paper suggests the buyer in determining the prices of different houses with the similarity of characteristics and properties. According to the survey made the prices of properties always rise every year, so a buyer needs a real estate forecasting system. This research paper can help estimate the price of a house which helps both seller and buyer for a reasonable price to buy a property. The result showed that machine learning algorithms in linear regressions model can accurately predict area price with high accuracy rates. However, some challenges such as property value based on area, characteristics, locality, facilities provided. It highlights the importance of",6.0,
d06c5551991c13921d2c5b2e3492ebf7526a9bd3,10.5089/9781484387764.001,Regional Labor Mobility in Spain,2018.0,Social Science Research Network,Lucy Liu,"This paper studies the main factors that explain the low regional mobility in Spain, with a view to identifying policy options at the regional and central level to promote labor mobility. The empirical analysis finds that house prices, labor market conditions, and the pervasiveness of labor market duality at the regional level are the main determinants for Spain’s regional mobility, while labor market institutions and policies play an important role at the national level. Policies that facilitate wage setting flexibility and reduce labor market duality could help enhance the functioning of the labor market, thereby promoting labor mobility. There may be also room for policies to incentivize people to move and provide support through targeted active labor market policies.",6.0,https://www.elibrary.imf.org/doc/IMF001/25633-9781484387764/25633-9781484387764/Other_formats/Source_PDF/25633-9781484392393.pdf
4f15678162fcfe537dfdb1bc9ab064f354953c2a,10.1177/00420980231224640,Those who leave: Out-migration and decentralisation of welfare beneficiaries in gentrified Paris,2024.0,Urban studies,Luc Guibard; R. Le Goix,"In major metropolitan areas, gentrification, financialisation and welfare retrenchment contribute to a severe housing crisis. Over the past 20 years, home price inflation and affordable housing shrinkage have been particularly acute in Paris. Such issues have been linked to the displacement of lower-income Parisians and the suburbanisation of poverty on a regional scale. In this article, we match disaggregated data from the Family Benefits Fund (CAF) with information on local housing markets, to empirically document these expulsionary processes. Our methodology is twofold. First, we investigate out-migration factors using logistic regressions. Second, we compare households’ changes in access to the city centre and urban resources following a move. Data show that social vulnerability is associated with a greater risk of leaving Paris and that housing welfare is playing a crucial role in mitigating this risk. Also, the higher the pressure on local housing markets, the more social inequalities determine mobility behaviour. Finally, beyond the effects of family structure, patterns of decentralisation are related to income level: less affluent households go farther from the city centre,",6.0,
cb863c5bc8e9b488ebd40ea4cd9dd4ed10a895df,10.1109/iconstem56934.2023.10142280,Improved Prediction Accuracy of House Price Using Decision Tree Algorithm over Linear Regression Algorithm,2023.0,2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM),Pammi Chandu; N. Devi,"As a direct outcome of this research, it is planned that the accuracy of house price projections will be enhanced by using a novel decision tree algorithm rather than linear regression. This will be done in order to achieve the desired result (LR). The N=10 iteration of the Decision Tree Algorithm is put to use in order to generate the prediction. The size of the sample is figured out with the use of a G power Calculator, and a cutoff of 80% is decided upon as the minimum need for sufficient analytical power. The Linear Regression Method can be found in Group 1, whereas the New Decision Tree Algorithm can be found in Group 2. The confidence interval for the pre-test power is from 95% to 80%, the alpha value is 0.05, the beta value is 0.2, and the total number of participants in the study is twenty. In contrast, the accuracy of the New Decision Tree (DT) Algorithm was 90%, while the accuracy of the Linear Regression Algorithm was 80%. The findings of the statistical analysis that was carried out with the assistance of SPSS showed that the value of accuracy was insignificant: p=0.618 (p>0.05). The Innovative Decision Tree Algorithm outperforms the Linear Regression approach when it c",6.0,
2d7f130b935d88f8e6331808b3ca723c2f192064,10.1080/09599916.2020.1740765,Restrictions versus amenities: the differential impact of home owners associations on property marketability,2020.0,,K. Goodwin; C. R. La Roche; B. Waller,"ABSTRACT Common-interest developments (CIDs) or planned urban developments (PUDs) can include a multitude of property types such as condos, townhomes, coops and single-family residences. Many such developments are privately governed by a homeowners’ association (HOA) and managed by an HOA board of directors comprised of community homeowners. While such communities and their governing bodies have been widely criticised for their onerous rules, regulations and exclusionary practices, many argue that the amenities, benefits and utility afforded to its members provide a large degree of satisfaction for homeowners. In fact, one of the purposes of an HOA is to preserve and enhance home values by creating an environment with minimal negative externalities. Although it is generally assumed that an HOA does add value, these associations have also generated a number of controversial disputes. This paper examines the effects of an HOA on marketability to empirically shed light on the question of whether buyers find HOAs to be beneficial or burdensome. The results show that the impact of the HOA on price, marketing time and probability of sale are not even across price segments and exist even ",6.0,
fc5bc85c76c69374f7265703f89bb906bb6b0689,10.1080/13658816.2022.2100891,Scalable spatiotemporal regression model based on Moran’s eigenvectors,2022.0,International Journal of Geographical Information Science,Hayato Nishi; Yasushi Asami; H. Baba; C. Shimizu,"Abstract We propose a scalable regression model with spatially and temporally varying coefficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and temporal locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation–maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, la",6.0,
f27075fa11f5ae37f80daecb2a72406132b1f8f6,10.1080/00330124.2022.2112966,"Urban Space, Mixed Land Use, and Residential Land Prices: A Case Study of Nanjing, China",2022.0,Professional Geographer,Feng-Lin Yuan; Y. Wei; Weiye Xiao,"Residential land price is a critical component of housing prices and affordability, and existing studies of its influencing factors emphasize location and accessibility. This study analyzes the effects of neighborhood land use, especially employment and services, on residential land prices in Nanjing, a subcenter in the Yangtze River Delta region, China. We find that residential land prices are positively associated with producer services, as well as public and commercial services, and negatively associated with manufacturing. Further analysis reveals the spatial heterogeneity of the impact of manufacturing on residential land prices, which become positive in suburban regions. The effects of neighborhood land use on residential land prices are sensitive to the distance to the central business district. The geographically and temporally weighted regression model uncovers the temporal heterogeneity that the positive impacts of public and commercial services on residential land prices have been decreasing while the producer service is playing a more significant role. The results of this study contribute to the literature by shedding light on the impact of neighborhood land use on resi",6.0,
71ac66a3e6a6e2422b2c881d3156a364df02c7d3,10.2478/remav-2025-0001,Housing Price Prediction - Machine Learning and Geostatistical Methods,2024.0,Real Estate Management and Valuation,R. Cellmer; Katarzyna Kobylińska,"Abstract Machine learning algorithms are increasingly often used to predict real estate prices because they generate more accurate results than conventional statistical or geostatistical methods. This study proposes a methodology for incorporating information about the spatial distribution of residuals, estimated by kriging, into selected machine learning algorithms. The analysis was based on apartment prices quoted in the Polish capital of Warsaw. The study demonstrated that machine learning combined with geostatistical methods significantly improves the accuracy of housing price predictions. Local factors that influence housing prices can be directly incorporated into the model with the use of dedicated maps.",6.0,https://reference-global.com/2/v2/download/article/10.2478/remav-2025-0001.pdf
5ec63f2ec16098bd6378adf4021304219cc7820a,10.2139/ssrn.3772087,Home Broadband and Human Capital Formation,2025.0,Social Science Research Network,R. Sanchis-Guarner; J. Montalbán; Felix Weinhardt,"This paper estimates the effect of home high-speed internet on national test scores of students at age 14. We combine comprehensive information on the telecom network, administrative student records, house prices and local amenities in England in a fuzzy spatial regression discontinuity design across invisible telephone exchange catchment areas. Using this strategy, we find that increasing broadband speed by 1 Mbit/s increases test scores by 1.37 percentile ranks in the years 2005-2008. This effect is sizeable, equivalent to 5% of a standard deviation in the national score distribution, and not driven by other technological mediating factors or school characteristics. JEL-Codes: J240, I210, I280, D830.",6.0,https://doi.org/10.1016/j.econedurev.2025.102679
c3337bcbcfb69d4f9f3d651af53a8b0f19c0fb67,10.11113/jt.v73.4337,An Overview of the Forecasting Methods Used in Real Estate Housing Price Modelling,2015.0,,Mohan Munusamy; C. Muthuveerappan; Maizan Baba,"Forecasting is very fundamental in real estate where the past transactions become the evidences while decision making for the present and the future. Several techniques and validation approached that were commonly used in housing price index forecasting. Beside the appropriate forecasting method, error calculation is one of the critical constraints in accuracy out of all methods. This paper overview the available methods and the types of error being considered in forecasting techniques. Then the forecasting methods, namely Multiple Regression Analysis (MRA) and Artificial Neural Network which are highly applied in forecasting modelling are compared over its error accuracy.",6.0,https://doi.org/10.11113/jt.v73.4337
80a5b36041bb40434b0f26d08ff02ad0f2a009d7,10.5539/ass.v4n9p84,Today’s Relevancy of the Migration Determinants Theory,2009.0,,Baayah Baba; Peter Mc Gregor; H. K. Jusoff,"There are many theories of migration. The human capital, search and the gravity models are among the most widely referred to by researchers. The influence of house prices, job vacancies, wage differentials on the migration decisions have widely been noted and have the expected signs. However, in a study done using the Scottish data the house price variables seem to give an opposite sign that contradicts previous findings. Perhaps comfortable house and available entertainment facilities matters and not just the house price that affects the decision to migrate. Future studies should include the entertainment facilities as one of the determining factor of migration.",6.0,https://ccsenet.org/journal/index.php/ass/article/download/1079/1039
e8d334146df3126332832d3e5734ff88a8a17493,10.5109/4055228,Land Value Capture: Defining Crucial Variables Difference-in-Differences Model for Residential Properties Surrounding MRT Jakarta Stage I,2020.0,Evergreen,M. Berawi; Alex Darmawan; Gunawan; P. Miraj; H. Rahman,"Positive effect on property value can be gained from the development of public transportation system. This study is aimed to identify the variables that affect the residential property price around the first phase of the Jakarta MRT station project construction. The variables were based on the data of Land & Building Tax Imposition Base (NJOP). From the statistical analysis (multi collinearity, paired T, ANOVA, and normality test), it is found that variables significantly affecting the residential property price are unit area, shared building, shared land, land area, building area, density, distance to MRT/CBD/hospital/favorite high school, and public park.",6.0,https://doi.org/10.5109/4055228
73a5a07c14b9eaa2f16f2aed52543289c7f14317,10.1080/10691898.2015.11889735,Rail Trails and Property Values: Is There an Association?,2015.0,,E. Hartenian; N. Horton,"The Rail Trail and Property Values dataset includes information on a set of n = 104 homes which sold in Northampton, Massachusetts in 2007. The dataset provides house information (square footage, acreage, number of bedrooms, etc.), price estimates (from Zillow.com) at four time points, location, distance from a rail trail in the community, biking score, and walking score. The dataset is amenable to use with exploratory data analysis in introductory courses, intermediate courses with a focus on visualization and multivariate relationships as well as advanced courses that utilize repeated measures regression models and more sophisticated graphics.",6.0,https://www.tandfonline.com/doi/pdf/10.1080/10691898.2015.11889735?needAccess=true&role=button
e87fc9647ef15c29aa82b112fd85bb656732c47e,10.1080/09599916.2020.1861066,Automated Valuation Services: A case study for Aberdeen in Scotland,2021.0,Journal of Property Research,Rainer Schulz; Martin Wersing,"ABSTRACT Automated valuation services (AVSs) offered by listings platforms predict market values based on property characteristics supplied by users. We investigate the implementation of such a service for the City of Aberdeen. We fit different market value models with machine learning methods and assess them in a rolling windows procedure that mimics an AVS setting. We also investigate the ease and robustness with which the models can be implemented. We discuss how prediction uncertainty can be measured and reported to users. If implemented in the future, such a service has the potential to improve the transparency of the local housing market.",6.0,https://aura.abdn.ac.uk/bitstream/2164/18150/1/Schulz_et_al_JPR_AutomatedValuationServices_AAM.pdf
3a1fbb33da06a3c5f3e4be0091954df9e0687b7f,10.3233/jifs-232121,Twin extreme learning machine based on heteroskedastic Gaussian noise model and its application in short-term wind-speed forecasting,2023.0,Journal of Intelligent & Fuzzy Systems,Shiguang Zhang; Dingkun Guo; Ting Zhou,"Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model.",6.0,
e8bae31e062b39fcfae928507d80a388b7fd2bc1,10.1109/iccosite57641.2023.10127705,Comparative Models of Price Estimation Using Multiple Linear Regression and Random Forest Methods,2023.0,"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)",Denny Jean Crosss Sihombing; Desi C. Othernima; Jonson Manurung; J. Sagala,"The house is one of the essential humans needs as a place to gather and do activities with family, and shelter, as well as a means of investment. The growth rate of people's demand for housing, especially houses in an area, is influenced by the rate of population growth in that area. Some regions in Indonesia with a reasonably high population growth rate are Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek). On the other hand, property entrepreneurs must be able to project house prices because businesses engaged in the property sector are currently very competitive. This study aims to model and compare several machine learning methods to estimate house prices in Jabodetabek based on facilities, year of construction, location, land and building area, number of rooms, condition of house construction, and legality documents. This modeling uses Multiple Linear Regression and Random Forest methods. The results of the modeling evaluation where the Random Forest model has an accuracy rate of 95.6%, while the Multiple Linear Regression model has an accuracy rate of 75%.",6.0,
7510ef6ee69a6e713f65993ee266878dd26c49e8,10.1177/0042098019872391,"Private government, property rights and uncertain neighbourhood externalities: Evidence from gated communities",2020.0,Urban studies,G. Turnbull; Velma Zahirovic-Herbert,"Economists traditionally view public and private land use regulation as alternatives to each other. An alternative view argues that public and private regulation are not equally suited to accomplish the same outcomes. In particular, government regulation is easily changed while private regulation is not, making the latter better suited to control future externality risk. One implication of the alternative view is that more risk-averse households are drawn to gated neighbourhoods while their less risk-averse counterparts are not. This paper exploits exogenous differences in neighbourhood amenity uncertainty created by public school attendance zone changes to test this prediction of the alternative view. The results show that greater exogenous amenity uncertainty yields stronger house price capitalisation in gated subdivisions than in open neighbourhoods, a pattern consistent with the risk-aversion sorting hypothesis. The results are robust and are consistent with the key implication of the alternative view of private regulation.",6.0,
6d49cc5766f594279354788417b8a7c4d2e06c0c,10.1108/ijhma-10-2019-0103,House price and household consumption in China: evidence from micro-level data,2020.0,,Xiaojun Yuan; Y. Shen; Hai-Gang Zhou,"Purpose - This paper aims to identify how house price affects household consumption. Design/methodology/approach - The authors use a micro-level data set that tracks the house price and consumption of a vast number of households over a period of four years. OLS regression is the main econometric method. Findings - The authors document robust evidence that an increase in house prices stimulates household consumption, regardless of whether a household owns or rents. Moreover, the authors find that both acquiring and losing homeownership negatively affects household consumption. Further investigation suggests significant regional heterogeneity in the relationship between house prices and household consumption. Originality/value - This is one of the first studies examining the relationship between house price and household consumption in China using micro-level data. Given the uniqueness of the Chinese housing market and China’s fast-growing consumption rate, the study contributes new evidence to the long-lasting debate.",6.0,
2f637117ce496a443127e83c8988b856d6045568,10.1007/978-3-030-65854-0_5,Analysis of hidden feedback loops in continuous machine learning systems,2021.0,International Conference on Software Quality. Process Automation in Software Development,A. Khritankov,"In this concept paper, we discuss intricacies of specifying and verifying the quality of continuous and lifelong learning artificial intelligence systems as they interact with and influence their environment causing a so-called concept drift. We signify a problem of implicit feedback loops, demonstrate how they intervene with user behavior on an exemplary housing prices prediction system. Based on a preliminary model, we highlight conditions when such feedback loops arise and discuss possible solution approaches.",6.0,https://arxiv.org/pdf/2101.05673
9c6a53540cebed3529d6e3a0fbb11bb2376ee69d,10.1080/20954816.2019.1691797,Housing policies in Greater China and Singapore,2020.0,,Yang Zhang; Qing-Yong Zhang; Huan-Huan Zheng,"Abstract This paper provides a comparative study of housing policies in the greater China area and Singapore. We trace out the key features of the housing markets in the Chinese mainland, the Hong Kong SAR, the Macao SAR, and Singapore and review policies adopted in these markets. Soaring housing prices may distort capital allocation efficiency as money flows into housing market instead of more productive sectors, which poses threat to long-term sustainability and economic growth. Governments in these four jurisdictions have implemented various macroprudential policies and regulations in an attempt to curb housing demand, however the effectiveness of housing policies varies across markets. The housing prices stabilise in Singapore but continue to rise in Greater China despite the introductions of various tightening policies. The policy effectiveness is contingent on not only the characteristics of the market but also on its design and implementations.",6.0,
ab5dc83d2fa2ebbd72d7238968a1f2f003282eb8,10.36948/ijfmr.2023.v05i02.1849,Predicting House Price with Deep Learning: A Comparative Study of Machine Learning Models,2023.0,International Journal For Multidisciplinary Research,S. Kumar; S. Bhatt; Hasan Phudinawala,"In the real estate market, one of the most important tasks is to accurately predict house prices, as this information can assist buyers and sellers in making more educated decisions about the value of their respective properties. In this investigation, we investigate the application of deep learning (DL) strategies for forecasting housing prices and evaluate how well these strategies perform in comparison to more conventional machine learning approaches.",6.0,https://www.ijfmr.com/papers/2023/2/1849.pdf
eb037c4b7e5fe61ea35f8a860594543cd2a53c56,10.1177/08912424211006174,Do Higher Land Costs for New Single-Family Housing Inhibit Economic Activity in U.S. Metropolitan Areas?,2021.0,Economic development quarterly,Robert W. Wassmer,"The price of a new home is greater if the land to put it on costs more. In many U.S. metropolitan areas, this generates the widely acknowledged equity concern that low- to moderate-income households spend disproportionately on housing. But high residential land prices translating into high single-family home prices may also generate the efficiency concern of discouraging new workers’ entry into such areas or encouraging existing workers’ exit. The result could be a decrease in economic activity. This research offers panel-data regression evidence in support of the existence of this adverse outcome. Perhaps these findings can raise the saliency of the needed state or federal government intervention to curtail the stringency of local residential land-use regulations. NIMBYs see these land-use regulations as in their jurisdiction’s best interest, but as demonstrated here, such restrictions impose additional metro-wide economic concerns.",6.0,
9da63085da8df15bc0095fde429139b95a305c5d,10.1111/1540-6229.12331,Local house price comovements,2020.0,Real Estate Economics,Marcel Fischer; R. Füss; Simon Stehle,We study the micro-level evolution of residential house prices using data on repeat sales on Manhattan Island from 2004 to 2015. We document that excess price comovement is a highly local and persistent phenomenon. The strength of such excess comovements vanishes with both spatial and temporal distance. Local underperformance is more persistent than local overperformance - particularly when house prices on aggregate level increase.,6.0,https://doi.org/10.2139/ssrn.3403551
dbc22646dd6ae067fa0471b51335dee3a86eb54a,10.1109/iciibms46890.2019.8991474,Prediction of House Price Based on RBF Neural Network Algorithms of Principal Component Analysis,2019.0,2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS),Li Xiao; T. Yan,"When the traditional BP neural network is used for prediction, the convergence speed is too slow, the prediction accuracy is low, and it is easy to fall into the local optimal solution. Aiming at these problems, a PCA-based RBF neural network prediction algorithm is proposed and verified. Firstly, PCA is used to recombine the influencing factors of housing prices to generate new comprehensive indicators. Then use the RBF neural network algorithm with strong approximation ability to model and predict the house price. The experimental results show that the fitting result of the predicted value and the real value is 97%, which can be used as an effective method for forecasting house prices.",6.0,
49748964576f4eaaa601b9c5592f1b09db8a9fa7,10.1016/j.enpol.2018.08.035,Property values and tax rates near spent nuclear fuel storage,2018.0,Energy Policy,Kirstin Munro; G. Tolley,Abstract This paper examines potential property value impacts when a nuclear power plant closes and spent fuel remains on site. We confirm earlier research that fails to find an impact on property values of proximity to a nuclear power site. Another contribution of this paper is our finding that a one percentage point increase in property taxes is associated with a 4.31% decrease in the sale price of a home. These results provide evidence for a positive impact of operating nuclear facilities on surrounding communities in the form of reduced residential property taxes for a given level of public expenditures.,6.0,
5ee3485a5ae0086c7a844b8533bd0d3e4e9e9395,10.1007/s10901-024-10133-6,"Optimizing housing price estimation through image segmentation and geographically weighted regression: an empirical study in Nanjing, China",2024.0,Journal of Housing and the Built Environment,Rui Wang; Yanhui Wang; Yu Zhang,,6.0,https://research.tue.nl/files/347508630/s10901-024-10133-6.pdf
d23b2d0782b8b90f0f4c85914657780935095d30,10.6107/jkha.2011.22.5.047,Impact Analysis of an Eco-Park on the Adjacent Apartment Unit Price by Using the Hedonic Model - With a Focus on the Cheongju Wonheung-ee Park and Adjacent Apartments -,2011.0,,Hye-Jin Ko; Ki-Bum Yun; Young-Ju Shim; Hee-yun Hwang,,6.0,https://doi.org/10.6107/jkha.2011.22.5.047
207e3d65ca15e0f4066b5480307854be13a3ac6a,10.1016/j.cacint.2025.100223,Exploring housing price dynamics in sustainable cities through a cooperated big data driven machine learning method: Case study on a Typical city in China,2025.0,City and Environment Interactions,Feng Han; Mei-Qu Lu; Donghong Qin; G. Zheng; Guihong Zeng; Yan Tan; Zhongyang Wu; Hai-Jian Lu; Jun Wang; Yirong Deng; Hui He,,6.0,https://doi.org/10.1016/j.cacint.2025.100223
2e07e31e3a38b997ea0aa3c6e6512941251cc326,10.1016/j.regsciurbeco.2023.103877,Parking not included: The effect of paid residential parking on housing prices and its relationship with public transport proximity,2023.0,Regional Science and Urban Economics,Anders Bondemark; A. Merkel,,6.0,https://doi.org/10.1016/j.regsciurbeco.2023.103877
bae103bb56a4e867202bee4c80298f07a96ec567,10.1111/grow.12496,"Rent burden determinants in hot and cold housing markets of Davidson and Shelby counties, Tennessee",2021.0,,M. Samarin; Madhuri Sharma,,6.0,
bbc1441970dc7f872d2f0a37b1bda004762f50ce,10.1166/asl.2018.10738,Prediction of Housing Price Index in Malaysia Using Optimized Artificial Neural Network,2018.0,,Saada Ali Mohamed Daradi; U. Yusof; N. Kader,,6.0,
e2134ce08ed4558757de069f5eea19bd83d50760,10.1016/j.frl.2023.104400,Community resilience and house prices: A machine learning approach,2023.0,Finance Research Letters,Yi Zheng,,6.0,
56fb7e702c21566b02d4e6581b546bb844325228,10.1080/00420987720080701,Determinants of Urban House Prices: A Methodological Comment,1977.0,,J. Mark,,6.0,
d393c65c0539a395ce8258e13c614e7749703528,10.1016/j.regsciurbeco.2018.02.005,Housing price indices for small spatial units,2018.0,,Esteban López; G. Hewings,,6.0,
9a88c0eef784fe1bd5a8c84513cd4963de8e4197,10.1007/978-3-642-31994-5_14,Dealing with Data at Various Spatial Scales and Supports: An Application on Traffic Noise and Air Pollution Effects on Housing Prices with Multilevel Models,2012.0,,J. le Gallo; C. Chasco,,6.0,
ebf80cfd13e864f1b2482c19826f76b660340cb1,10.11820/dlkxjz.2014.03.009,Determinants and dynamics of spatial differentiation of housing price in Yangzhou,2014.0,,Yang Wang; Qiang Li; Jing Qin; Shao-Jian Wang,,6.0,
e6f814bd1b9579d4b8b8bd837b804d6fc45b1591,10.1007/s00168-020-01032-z,The impact of land use regulation across the conditional distribution of home prices: an application of quantile regression for group-level treatments,2020.0,The annals of regional science,Tammy Leonard; Xi Yang; Lei Zhang,,6.0,
7c19f9988af184fb4878f1387e1716bdb8814cc5,10.1007/s12061-023-09530-1,"Investigating the Determinants of Housing Rents in Hangzhou, China: A Spatial Multilevel Model Approach",2023.0,Applied spatial analysis and policy,D. Zhan; Chunxin Xie; Juan-Feng Zhang; Bin Meng,,6.0,
316c30d2e4f2d221113acdec03718479dcb6fd54,10.1007/s12061-023-09514-1,"Impact of Mixed Land Use on Housing Prices, Spatial Differentiation and Implications: Empirical Analysis Based on Qingdao",2023.0,Applied spatial analysis and policy,Yuan Gao; Chang-Chun Feng,,6.0,
1e4327e93f1787b12468bedf76325864b1996ff5,10.1080/02815739508730383,Property values and health risks: The willingness to pay for reducing residential radon radiation,1995.0,,T. Söderqvist,,6.0,
3fede3437f05f8b8058d3a20adbd20f2ed9e0858,10.1111/1540-6229.00826,The Effect of Regional Growth Characteristics on Regional Housing Prices,1977.0,,C. P. Pritchett,,6.0,
ff938e08700c1a83ddb3670199d1b2f9ea7bd80c,10.3390/ijgi13110396,Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing,2024.0,ISPRS Int. J. Geo Inf.,Le Chen; Simin Yu; Shi Shen; You Wan; Changqing Song,"The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, there is a lack of understanding of the impact of environmental conditions and human activity on the vegetation’s carbon sequestration ability, especially in highly urbanized areas. For example, effective vegetation management methods can enhance vegetation Gross Primary Productivity, while emissions of air pollutants like O3, CO, NO2, and PM2.5 can suppress it. This paper mainly explores the factors influencing vegetation carbon sequestration capacity across different regions of Beijing. Based on remote sensing data and site observation data, this paper analyzed the spatiotemporal variation trend of Annual Gross Primary Production (AGPP) and the influence of environmental factors and human activity factors on GPP in Beijing from 2000 to 2020 by using the Theil−Sen’s slope estimator, Mann−Kendall trend test, and comparing Geographically Weighted Regression method (GWR) and Geogr",5.0,https://doi.org/10.3390/ijgi13110396
5f3aa80189f514038e63f0fff65efbb4bd6bf7ae,10.1108/ijoem-10-2020-1272,Loan to value ratio and housing loan default – evidence from microdata in India,2022.0,International Journal of Emerging Markets,A. Saha; D. Rooj; R. Sengupta,"PurposeThis study aims to investigate the factors that drive housing loan default in India based on unique micro-level data drawn from a public sector bank's credit files with a national presence in India. The authors address endogeneity in the loan to value ratio (LTV) while deciphering the drivers of default.Design/methodology/approachThe study uses a probit regression approach to analyze the relationship between the probability of default and the explanatory variables. The authors introduce two instrumental variables to address the issue of endogeneity. The authors also add state-level demographic and several other control variables, including an indicator variable that captures the recent regulatory change. The authors’ analysis is based on 102,327 housing loans originated by the bank between January 2001 and December 2017.FindingsThe authors find that addressing the endogeneity issue is essential to specify default drivers, especially LTV, correctly. The nature of employment, gender, socio-religious category and age have a distinct bearing on housing loan defaults. Apart from the LTV ratio, the other key determinants of default are the interest rate, frequency of repayment, pr",5.0,
2b76051dba4aa3717c132029de813000bcb4f62a,10.1108/jfmpc-04-2019-0033,An exploratory approach for enhancing vertical and horizontal equity tests for ad valorem property tax valuations using geographically weighted regression,2019.0,Journal of Financial Management of Property and Construction,P. Bidanset; M. McCord; P. Davis; Mark A. Sunderman,"
Purpose
The purpose of this study is to enhance the estimation of vertical and horizontal inequity within property valuation. Property taxation is a crucial source of finance for local government around the world – based on a presumptive tax base underpinned by estimates of property value, inaccurate real estate valuations used for such ad valorem or value-based property tax calculations potentially lead to a variety of costs, both financial and other, for tax payers and governments alike. More common are increased costs in time, staff and, in some cases, legal fees. Some governments are even bound by acceptability thresholds to promote fairness, equitability and overall government accountability with respect to valuation.


Design/methodology/approach
There exist a number of vertical inequity measurements that have undergone academic testing and scrutiny within the property tax industry since the 1970s. While these approaches have proved successful in detecting horizontal and vertical inequity, one recurring disadvantage pertains to measurement error/omitted variable bias, stemming largely from a failure to accurately account for location. A natural progression within property ta",5.0,https://pure.ulster.ac.uk/en/publications/46708da4-4c69-4185-b702-354321fe0042
cbe22e39a5c675fa0a242d856aed571c08638f33,10.57255/intellect.v4i1.1385,"Analisis Perbandingan Prediksi Harga Rumah Dengan Random Forest, Gradient Boosting, dan XGBoost",2025.0,Intellect,B. Wulansari; Donni Prabowo,"House price prediction poses a significant challenge in the property sector, especially in the Yogyakarta region, which exhibits a wide range of price variations. This study aims to compare the performance of three regression algorithms such as Random Forest, Gradient Boosting, and XGBoost, in building predictive models based on features such as land area, building area, number of bedrooms, bathrooms, and garage availability. The dataset analyzed consists of 1,642 entries, with house prices ranging from IDR 7 million to IDR 4.37 billion, an average price of IDR 1.14 billion, and a mode of IDR 775 million. Model evaluation was conducted using Mean Squared Error (MSE) and the coefficient of determination (R²), where XGBoost achieved the best performance with an MSE of 1.56 × 10¹⁴ IDR², an R² of 0.7746, and a Root Mean Squared Error (RMSE) of approximately IDR 12.5 million. These results indicate that XGBoost outperforms the other two models in handling complex tabular data and provides more accurate predictions. The predictive model has practical potential to be utilized by property developers, real estate agents, and local governments as a decision-support tool for price estimation,",5.0,https://journal.makwafoundation.org/index.php/intellect/article/download/1385/875
e16104daf2657a49700986ad92033c4af003bb02,10.3390/su17094203,Research on Spatial Differentiation of Housing Prices Along the Rail Transit Lines in Qingdao City Based on Multi-Scale Geographically Weighted Regression (MGWR) Analysis,2025.0,Sustainability,Yan-Jun Wang; Zi-Xuan Liu; Ya-Wen Wang; Peng Dai,"Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air pollution, but also significantly reduces residents’ commuting time, broadens urban accessibility, and reshapes the decision-making basis for residents when choosing residential locations. This study takes the 1st, 2nd, 3rd, 4th, 8th, 11th, and 13th metro lines that have been opened in Qingdao City as examples. It selects 12,924 residential samples within a 2 km radius along the rail transit lines. By using GIS spatial analysis tools and the multi-scale geographically weighted regression (MGWR) model, it analyzes the spatial differentiation characteristics of housing prices along the rail transit lines and the reasons and mechanisms behind them. The empirical results show that housing prices decrease to varying degrees with the increase in the distance from the rail transit. For every additional 1 km from the rail transit station, the housing price increases by 0.246%. Through model comparison, it was found that MGWR ",5.0,https://www.mdpi.com/2071-1050/17/9/4203/pdf?version=1746543627
2661cf8b87138bea845c729bf21335a2eadce031,10.1111/1540-6229.12352,Environmental hazards: The microgeography of land‐use negative externalities,2021.0,Real Estate Economics,T. Somerville; Jake Wetzel,"The decisions on the siting of hazardous facilities and compensation for nearby landowners depends on an accurate estimation of the negative externalities these facilities place on proximate land uses, primarily residential properties. In this paper we highlight the sensitivity of these estimates to the treatment of distance from the hazard and to the presence of other nearby land uses identified at a highly granular geographic level. Recent opposition to the expansion of North American pipeline capacity has been intense, mixing concerns about climate change, environmental damage, and local opposition to the physical presence of pipelines in their neighbourhoods. This paper studies the disamenity effects associated with the last factor. In doing so we generate results that more broadly address the specification and left out variable bias challenges of measuring the capitalization of negative location-specific environmental externalities. The key contributions of this paper are first showing that disamenity effects can be highly localized and easily susceptible to errors with parametric specifications. Second, that the magnitude of the effect on house prices arising from proximity a",5.0,http://hdl.handle.net/2429/63153
89b96d5689ea679003aa52a35d73e0b1da69e05b,10.1007/s00267-023-01813-0,"Impact of COVID-19 Restrictions on the Urban Thermal Environment of Edmonton, Canada",2023.0,Environmental Management,Carolyne Qiang; J. Leydon; Yuhong He,"The effects of the COVID-19 pandemic on urban environments are addressed in many recent studies. However, limited research has been conducted to examine the impact of the pandemic on anthropogenic emissions over urban land use types, and their relation to socioeconomic characteristics. Anthropogenic heat, as the main contributor to the urban temperature, is changed by the sudden halt imposed by COVID-19 lockdowns. This study thus focuses on previously under-explored urban thermal environments by quantifying the impact of COVID-19 on urban thermal environments across different land-use types and related socioeconomic drivers in Edmonton, Canada. Using Landsat images, we quantified and mapped the spatial pattern of land surface temperature (LST) for business, industrial, and residential land use areas during both the pandemic lockdown and pre-pandemic periods in the study area. Results show that temperature declined in business and industrial areas and increased in residential areas during the pandemic lockdown. Canadian census and housing price data were then used to identify the potential drivers behind the LST anomaly of residential land use. The most important variables that affe",5.0,https://link.springer.com/content/pdf/10.1007/s00267-023-01813-0.pdf
a8317276d6b372bd4baa6d5e37d252974b3d92b5,10.1108/ijhma-06-2022-0087,Spatial analysis of housing prices in Tehran city,2022.0,International Journal of Housing Markets and Analysis,S. Hosseini; Behnaz Bahadori; Shahram Charkhan,"
Purpose
The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).


Design/methodology/approach
In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.


Findings
The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices th",5.0,
07cd26d2e72bba4908f9c5bf654a42480ff2a8da,10.3389/fpubh.2025.1598394,Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai,2025.0,Frontiers in Public Health,Xingxing Yin; Jinghang Cui; Yifan Wu; Mingxuan Cui; Kun Li; Haoxiang Guo,"Background Shanghai is one of the first Chinese cities to tackle the challenges presented by an aging population. In response, the city has been actively seeking solutions for older adult care within a metropolitan context. This study is based on data from 1,272 older adult care institutions in Shanghai. It uses spatial analysis methods to visually display the spatial evolution of older adult care institutions in the city and analyzes the influencing factors of their spatial distribution patterns using a geographic detector approach. Methods The research methodology used in the study includes Kernel Density Estimation for visualizing the spatial distribution of older adult care institutions in Shanghai, the Rand Index for measuring the match between older adult care institutions and the older adult population, and Geographically Weighted Regression for addressing spatial heterogeneity by providing local estimates for regression coefficients at different geographical locations. Results The number of older adult care institutions in Shanghai has seen significant growth over the past 20 years, increasing fourfold due to the rise in both public and private facilities. Older adult care ",5.0,https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1598394/pdf
df399efb2d475d58683b28ff674da8c7150546a0,10.1289/ehp13393,Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models,2024.0,Environmental Health Perspectives,H. Ohanyan; M. A. van de Wiel; L. Portengen; A. Wagtendonk; N. D. den Braver; T. D. de Jong; Monique W. M. Verschuren; K. van den Hurk; K. Stronks; Eric P. Moll van Charante; N. V. van Schoor; C. Stehouwer; A. Wesselius; A. Koster; M. ten Have; B. Penninx; M. V. van Wier; I. Motoc; A. Oldehinkel; G. Willemsen; D. Boomsma; M. Beenackers; A. Huss; M. V. van Boxtel; G. Hoek; J. Beulens; R. Vermeulen; J. Lakerveld,"Background: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. Objectives: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. Methods: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688–141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for",5.0,https://doi.org/10.1289/ehp13393
5c2b1e21701b7451eded3afacf3ec427c82cc2f3,10.3390/f16111713,Urban Greening Strategies and Ecosystem Services: The Differential Impact of Street-Level Greening Structures on Housing Prices,2025.0,Forests,Qian Ji; Shengbei Zhou; Longhao Zhang; Yankui Yuan; Lun-Sai Wu; Feng-Liang Tang; Jun Wu; Yufei Meng; Yu-Qiao Zhang,"Street greening is widely recognized as influencing resident well-being and housing prices, and street-view imagery provides a fine-grained data source for quantifying urban microenvironments. However, existing research predominantly relies on single indicators such as the Green View Index (GVI) and overall green coverage/volume lacking a systematic analysis of how the hierarchical structure of trees, shrubs, and grass relates to housing prices. This study examines the high-density block context of Tianjin’s six urban districts. Using the Street Greening Space Structure (SGSS) dataset to construct greening structure configurations, we integrate housing-price data, neighborhood attributes, and 13,280 street-view images from the study area. We quantify how “visibility and hierarchical ratios” are capitalized on in the housing market and identify auditable threshold ranges and contextual gating. We propose an urban–forest structural system centered on visibility and hierarchical ratios that links street-level observability to ecosystem services. Employing an integrated framework combining Geographical-XGBoost (G-XGBoost) and SHapley Additive exPlanations (SHAP), we move beyond average",5.0,https://doi.org/10.3390/f16111713
628322968508ab7cf6a3b2451273591998e10da5,10.1200/jco.2022.40.16_suppl.6511,Racial and socioeconomic disparities in telemedicine use among US patients initiating cancer treatment during the COVID-19 pandemic.,2022.0,Journal of Clinical Oncology,J. Guadamuz; Xiao-Liang Wang; T. Royce; G. Calip,"6511 Background: The COVID-19 pandemic was associated with declines in in-person clinical visits. While telemedicine visits have increased, uptake has varied. Here we assess demographic and socioeconomic factors associated with telemedicine use among patients initiating treatment for 21 common cancers at community oncology clinics. Methods: This retrospective study uses the nationwide Flatiron Health electronic health record-derived de-identified database of patients with cancer. Patient characteristics were determined using structured and unstructured data curated via technology-enabled abstraction. We included patients (≥ 18 years) who initiated first-line cancer treatment between March 2020 and September 2021 (follow-up through December 2021). We focused on differences in telemedicine use (≥ 1 telemedicine visit within 90 days after treatment initiation) across race/ethnicity, insurance coverage, rurality (per Rural-Urban Commuting Areas), and socioeconomic status (SES). SES was defined using census block group data from the American Community Survey (2015-2019) (quintiles representing least to most affluent areas) based on patient addresses and measured using the Yost Index (in",5.0,
c52a969cb0b6334c29e2719183e003313b8c0c80,10.1038/s41598-025-15294-0,An exploration of the spatial and temporal factors influencing industrial park vitality using multi-source geospatial data,2025.0,Scientific Reports,Xiao Ding; Fang Zheng; Guifen Lyu; Bo-Tao Feng,"Strengthening the multi-dimensional vitality of industrial parks is crucial for fostering social cohesion. However, previous researches mainly focused on the vitality of various functions, lacking detailed insights for the specific planning of industrial parks. To address this issue, this study combines the spatial regression and multi-scale geographically weighted regression models to systematically analyze the spatial and temporal variations of multidimensional vitality in industrial parks of Shenzhen city. Both real and virtual indicators are employed to measure the physical and digital vitality of the industry parks, distinguishing vitality variations across weekdays and weekends. Additionally, the study further investigates the relationships between the vitality and the other influencing factors. The findings reveal that the spatial distribution of real vitality and weekend vitality follows a polycentric clustering pattern, while weekday vitality exhibits a relatively uniform spatial distribution. Virtual vitality, characterized by the lowest vitality level, spreads outward from a core of the highest values to the periphery. Weekday vitality reaches its peak in the morning, wh",5.0,https://doi.org/10.1038/s41598-025-15294-0
881cd34465754e6d220098adbe47ba0fb35c54b7,10.3390/land13010101,"Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China",2024.0,Land,Junfeng Wang; Shaoyao Zhang; Wei Deng; Qianli Zhou,"The ongoing urban spatial transformation contributes to a more intricate and varied spatiotemporal correlation pattern between metropolitan expansion and the migrant population. In this study, the coupled coordination model (CCD) is applied to enable the quantification of the spatiotemporal correlation index of metropolitan expansion and migrant population from 2010 to 2020. Moreover, various correlation patterns are identified in this research, and the multiscale geographical weighted regression model (MGWR) is employed to examine the spatiotemporal heterogeneity of the influencing factors that contribute to this correlation. The research findings reveal the following insights: (1) The built-up areas increased twofold between 2010 and 2020, with an evident tendency toward southward expansion. In addition, population migration indicates slow migration in the inner metropolitan area and accelerated migration in the outer metropolitan area. The correlation between metropolitan expansion and migrant population follows a central–peripheral layer pattern that is characterized by a low-high-low progression. (2) While the balanced development of public services has somewhat decreased the ",5.0,https://www.mdpi.com/2073-445X/13/1/101/pdf?version=1705412683
55ae1b21d2a56b0d7cdbe5f6ec97ab06865bc1b4,10.1371/journal.pone.0285397,An ecological analysis of walkability and housing affordability in Canada: Moderation by city size and neighbourhood property type composition,2023.0,PLoS ONE,Chelsea D. Christie; C. Friedenreich; Jennifer E. Vena; D. Doiron; G. McCormack,"The neighbourhood built environment can support the physical activity of adults regardless of their individual-level socioeconomic status. However, physical activity supportive (walkable) neighbourhoods may not be accessible to those with lower incomes if homes in walkable neighbourhoods are too expensive. The objectives of this study were: 1) to estimate the associations between neighbourhood walkability and home values in Canadian cities, and 2) to test whether these associations differ by city size and residential property type composition within neighbourhoods. We linked built environment data from the 2016 Canadian Active Living Environments (Can-ALE) index with neighbourhood-level structural home characteristics and sociodemographic data from the 2016 Canadian census for 33,026 neighbourhoods across 31 Census Metropolitan Areas. We used multilevel linear regression models to estimate covariate-adjusted associations between neighbourhood walkability and natural-log median home values and tested city size and neighbourhood property type composition as moderators. There were no statistically significant associations between walkability and home values overall. The associations b",5.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0285397&type=printable
63f18f50e72aa07849e0ef5b775fc0c39a740632,10.3390/su12125051,Infrastructures and Sustainability: An Estimation Model for a New Highway Near Genoa,2020.0,Sustainability,P. Rosasco; Leopoldo Sdino,"The economic development of a territory is strongly correlated to its level of infrastructure (railway, roads, etc.); the complexity of this type of works requires careful planning and design that cannot be separated from the assessment of the impacts generated on citizenship affected by the new infrastructures. This study deals with the instrument defined by the Liguria Region for the implementation of infrastructures through the instruments called “Programmi Regionali di Intervento Strategico—P.R.I.S.” (Regional Strategic Intervention Programs) established by the Regional Law n. 39/2007. The aim of the P.R.I.S. is to guarantee the social protection of citizens that reside (as owners or tenants) or carry out economic activities in real estate units incompatible with the construction of the infrastructure, according to the main Italian law (Presidential Decree n. 327/2001) about the expropriation of private real estate for the construction of public works. In particular, the construction of a new link of the A7-A10-A12 motorway sections near the city of Genoa (called “Gronda”) is considered. The new infrastructure involves the expropriation of about 100 residential units and the re",5.0,https://www.mdpi.com/2071-1050/12/12/5051/pdf?version=1592734276
464ed9f3036b124d236efefebfca78d0d8554d8c,10.2139/ssrn.3010330,"Identifying House Price Booms, Bubbles and Busts: A Disequilibrium Analysis from Chaos Theory",2020.0,Journal of Mathematical Finance,G. Hanweck,"This research was motivated by the excesses of public policy since 2008 in an attempt to re-inflate the housing markets. Is it even possible or desirable to utilize such a vast amount of public resources to inflate a single sector such as housing that suffered from such a spectacular bubble and collapse? The consequences suggest that, as a way to bolster real household incomes and aggregate output, these policies have disappointed. In contrast, there is a fear that the monetary stimuli will lead to unsustainable housing price inflation, if not a bubble. I address these questions in the analysis from the standpoint of determining the stable equilibrium and sustainable house price appreciation rates consistent with the growth of median household income. The problem of identifying stable house price appreciation is to first identify the major proximate determinants of household demand for housing. A second is to show empirically the movement, deviation, and variation of these factors over time compared to housing prices. I use median household income as the major demand factor for houses and median single family house prices as an indicator of the price. A third is determining the sta",5.0,https://doi.org/10.2139/ssrn.3010330
2c5c44bfff9f8b0b5676e4d443b97123a5c96f67,10.1038/s41598-025-13757-y,"Seasonal disparities in green exposure under the 15-minute city framework: a case study of Xi’an, China",2025.0,Scientific Reports,Bo-Cheng Zhang; Tao Jiang,"Green exposure levels are closely related to human well-being. However, previous studies have rarely considered the green exposure experienced through human mobility, especially within the framework of the 15-minute city. Additionally, due to the lack of seasonal and large-scale spatial data, seasonal variations in green exposure have often been overlooked. In this study, we propose an assessment framework for seasonal green space exposure inequality within the context of the 15-minute city. By integrating the Green View Index, spatial statistical methods, deep learning, and publicly available urban housing price big data, we analyzed green exposure inequality in Xi’an, China. The results indicate that: (1) There is a seasonal inequality in the exposure to greenery within the 15-minute residential areas of Xi’an. At short distances (5–10 min), green exposure in summer is higher than that in winter, whereas at longer distances (15–30 min), green exposure in winter exceeds that in summer. (2) There is an inequitable relationship between urban housing prices and seasonal green exposure. In short-distance living circles, housing prices are negatively correlated with seasonal difference",5.0,https://www.nature.com/articles/s41598-025-13757-y.pdf
59a0e8d0957c6dadf5b55800e76492155a1325d1,10.3389/fpubh.2023.1287999,The impact of urban spatial environment on COVID-19: a case study in Beijing,2024.0,Frontiers in Public Health,Zhen Yang; Jiaxuan Li; Yu Li; Xiao-Wen Huang; Anran Zhang; Yue Lu; Xu Zhao; Xueyan Yang,"Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities’ ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, ",5.0,https://www.frontiersin.org/articles/10.3389/fpubh.2023.1287999/pdf?isPublishedV2=False
c828862389034afb7d2104d67535ddef1b307808,10.52223/jess.2024.5231,"The Impact of Landfill Proximity on Residential Property Values in Faisalabad, Pakistan",2024.0,Journal of Education and Social Studies,Ammara Nazeer; Tahira Sadaf; Ayesha Rouf; M. A. Iqbal; Komal Azhar,"A landfill is where trash is disposed of. Although landfills contribute to the cleanliness of our communities, they also represent a significant risk to public health and the cost of living for nearby individuals. Around the world, many people agree that having a property near a dumpsite affects the price or rent of the home in one of two ways: favorably or adversely. The study's primary objective was to investigate the influence of landfill proximity on housing prices. Data were collected through a well-structured questionnaire administered to a random sample of 200 households. Results revealed several significant findings. The distance from the landfill site significantly negatively impacts housing prices (? = -0.177, p < 0.05). Specifically, properties located closer to landfills are valued lower, indicating the detrimental impact of landfills on house prices close to residential areas. Environmental variables highlight significant community concerns. Most respondents reported an increase in pollution since the dump's operation and 95% believe the landfill negatively affects neighborhood attractiveness. Additionally, 90% of respondents feel bothered by bad odors, and 81% are con",5.0,https://www.scienceimpactpub.com/journals/index.php/jess/article/download/905/530
90bf51e1887239db4648cac241aff125d68e070a,10.1108/ijhma-01-2025-0010,A hybrid machine learning approach for housing price prediction: the stacking regressor method,2025.0,International Journal of Housing Markets and Analysis,Ömer Gökberk Erbulut; Zeynep Çolak,"Purpose
This study aims to explore alternative methodologies by comparing popular and effective machine learning models for housing price prediction. The primary objective is to develop a hybrid Stacking Regressor model combining multiple regression algorithms to leverage their strengths through a meta-model, thereby enhancing prediction accuracy.

Design/methodology/approach
The performance of widely used machine learning algorithms, including CatBoost, XGBoost, Random Forest, Extra Trees, Hist Gradient Boosting and Gradient Boosting, was evaluated using various error metrics for housing price prediction. Feature engineering and parameter optimization were applied to improve model performance, resulting in significant enhancements, particularly for Random Forest and Extra Trees. Furthermore, a Stacking Regressor model was constructed by integrating multiple regression algorithms to capitalize on their collective predictive capabilities.

Findings
The results indicate that CatBoost achieved the lowest error rates among the evaluated models. Random Forest and XGBoost also performed comparably, whereas Gradient Boosting exhibited higher error rates. The hybrid Stacking Regressor mode",5.0,https://acikerisim.comu.edu.tr/bitstreams/9955b279-fdc9-4fe6-9084-052b063bd0fb/download
92b46b1af05d9ee61c610c34906cea2b29aba1f7,10.1016/j.jamda.2025.105890,Trends in Assisted Living and Memory Care Supply From 2019–2023,2025.0,Journal of the American Medical Directors Association,Eric Jutkowitz; Gauri Gadkari; Erh-Chi Hsu; O. Tunalilar; L. Smith; Cassandra L. Hua; Jennifer N. Bunker; Kali S. Thomas,"Objectives: Describe geographic variation from 2019–2023 in assisted living (AL) and memory care supply, and its correlation with county-level characteristics. Design: Descriptive study of the supply of AL and memory care. Setting and Participants: Licensed AL communities in the United States (US) operating in 2019 and 2023. Methods: Data come from a national list of licensed ALs and the US Census Bureau’s American Community Survey. The primary outcomes of interest were AL supply and memory care supply (beds per 1000 adults age 65+ at the county level). We descriptively evaluated county characteristics by AL supply in 2019 and the change in AL supply from 2019 to 2023. Results: In 2023, counties with the highest AL and memory care supply were more likely to have greater wealth, higher educational attainment, and were urban. Between 2019–2023, 43% of counties had a decrease in AL supply, 35% of counties had no change in AL supply, and 22% of counties had an increase in AL supply. Counties with a decrease or no change in AL supply compared to increase in AL supply had a larger proportion of the population 65+ years of age, lower median household income, and were more rural. Between 2",5.0,https://doi.org/10.1016/j.jamda.2025.105890
468cef6abc4a4e7be447e289eb51b23ff72fc847,10.17645/up.v7i4.5771,Planning and Architecture as Determining Influences on the Housing Market: Budapest–Csepel’s Post–War Housing Estates,2022.0,Urban Planning,T. Egedy; B. Szabó; Hlib Antypenko; M. Benkő,"In Hungary, post-war housing estates can be categorised according to the time of their construction. Thanks to the development of construction technologies and urban planning, these so-called generations of housing estates demonstrate different features with regard to their physical layout and socio-economic characteristics. Socio-economic transformation that took place after the change of regime (1989) was widely affected by the physical parameters of these neighbourhoods and their dwelling stock. Our results show that different generations of housing estates have followed distinct trajectories in the housing market; thus, in addition to their geographical location within the city, planning, architecture, design, and the dwelling stock play a significant role in the market positions of these generations of housing estates. House prices have risen rapidly in Budapest since 2014 up until the pandemic in 2020, and housing estates became popular segments of the housing market. The main aim of this article is to investigate the role of urban planning, architecture and the built environment in this real estate process. The research is based on empirical real estate investigations, stati",5.0,https://www.cogitatiopress.com/urbanplanning/article/download/5771/5771
beed0618a53d099ecb801015c9f65a264ab9aa4b,10.3390/su17125441,"Exploring the Spatiotemporal Associations Between Ride-Hailing Demand, Visual Walkability, and the Built Environment: Evidence from Chengdu, China",2025.0,Sustainability,Rui Si; Yao-Yu Lin,"Ride-hailing services have reshaped urban commuting patterns, yet the spatiotemporal mechanisms linking built environment features to ride-hailing demand remain underexplored. Existing studies often overlook the joint effects of origin–destination visual walkability. This study integrates ride-hailing GPS trajectories and geospatial data to quantify mobility patterns and built-environment indicators in Chengdu, China. A dual analytical framework combining global regression and localized modeling was applied to disentangle spatial–temporal influences of urban form and socioeconomic factors. The results reveal that population density, floor–area ratio, and housing prices positively correlate with demand, while road density and distance to city center exhibit negative associations. Visual walkability metrics show divergent effects: psychological greenery and pavement visibility reduce ride-hailing usage, whereas outdoor enclosure enhances it. Temporal analysis identifies time-dependent impacts of built environment variables on main urban area travel. Housing price effects demonstrate spatial globality, while population density and city-center proximity exhibit geographically bounded c",5.0,https://www.mdpi.com/2071-1050/17/12/5441/pdf?version=1749797844
dca2a237bc7638f5e0f8140b1a81efcac2f30db4,10.12928/jampe.v3i1.9078,Millennial Generation (Gen-Y) Preferences Towards Landed House Ownership in Yogyakarta Urban Agglomeration Using Logistic Regression,2024.0,JAMPE (Journal of Asset Management and Public Economy),F. Prativi; N. Yuniarti; I. Kamara,"The city of Yogyakarta has become a magnet for the millennial generation (Gen-Y), leading to increased urbanization as residents flock to the city. This surge has resulted in a growing demand for land to accommodate public facilities, social amenities, and housing for workers. Despite soaring land prices, driven by high demand, land stocks have not diminished. Over the last 16 years, land prices have escalated by 30 times. However, the wages of Gen-Y formal workers in the DIY region stand at IDR 2,361,434, with an annual increase of only 8.51%. This rapid growth in property prices has not kept pace with the income growth of the millennial generation, raising concerns about their ability to access landed house ownership. This study aims to identify the preferences of the millennial generation regarding landed house ownership in the Yogyakarta Urban Agglomeration. The analytical method employed is Logistic Regression, involving 125 respondents of Gen-Y workers aged 27 to 41 years in the Yogyakarta Urban Agglomeration. Seven variables, encompassing 25 categorical predictors, were considered. The significant indicators influencing Gen-Y preferences in landed house ownership include the",5.0,http://journal2.uad.ac.id/index.php/JAMPE/article/download/9078/4119
4b2f8e9c3bd35a6e6c8ac9a364e17931431bb092,10.1186/s40537-023-00786-5,Online listing data and their interaction with market dynamics: evidence from Singapore during COVID-19,2023.0,Journal of Big Data,Jieun Lee; Kwan-Ok Lee,"With the emergence of Property Technology, online listing data have drawn increasing interest in the field of real estate–related big data research. Scraped from the online platforms for property search and marketing, these data reflect real-time information on housing supply and potential demand before actual transaction data are released. This paper analyzes the interactions between the keywords of online home listings and actual market dynamics. To do so, we link the listing data from the major online platform in Singapore with the universal transaction data of resale public housing. We consider the COVID-19 outbreak as a natural shock that brought a significant change to work modes and mobility and, in turn, consumer preference changes for home purchases. Using the Difference-in-Difference approach, we first find that housing units with a higher floor level and more rooms have experienced a significant increase in transaction prices while close proximity to public transportation and the central business district (CBD) led to a reduction in the price premium after COVID-19. Our text analysis results, using the natural language processing, suggest that the online listing keywords",5.0,https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-023-00786-5
9276e951ce39074954a3e4f645cec48038bc933f,10.3390/systems13070513,Real Estate Market Forecasting for Enterprises in First-Tier Cities: Based on Explainable Machine Learning Models,2025.0,Syst.,Dechun Song; Guohui Hu; Hanxi Li; Hong Zhao; Zongshui Wang; Yang Liu,"The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market regulation and enterprise investment decisions. This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. The results reveal the following: (1) In terms of commercial housing sales area, adequate housing supply, robust medical services, and high population density boost the sales area, while demand for small units reflects buyers’ balance between affordability and education. (2) In terms of commercial housing average sales price, growth is driven by education investment, population density, and income, with loan int",5.0,https://www.mdpi.com/2079-8954/13/7/513/pdf?version=1750858226
fa1652e6bd70bcbbd15e3ddebd6065aac247b07f,10.1108/ijoem-10-2020-1157,Club convergence and drivers of house prices across Turkish cities,2021.0,International Journal of Emerging Markets,Lokman Gunduz; M. Yılmaz,"PurposeThis paper aims to examine the convergence pattern of residential house prices in a panel of 55 major cities in Turkey over the period between 2010 and 2018 and to investigate the determinants of convergence club formations.Design/methodology/approachThe authors applied the log t-test to identify the convergence clubs and estimated ordered logit model to determine the key drivers.FindingsThe results suggest that there are five convergence clubs and confirm the heterogeneity of the Turkish housing market. Istanbul, the commercial capital, and Mugla, an attractive tourist destination, are at the top of the housing market and followed by the cities located in the western part, particularly along the Aegean and Mediterranean coasts of Turkey. Moreover, the ordered logit model results point out that the differences in employment rate, climate, population density and having a metropolitan municipality play a significant role in determining convergence club membership.Practical implicationsLarge-scale policy measures aiming to increase employment opportunities in rural cities of central and eastern provinces and providing lower land prices and property taxes in the metropolitan cit",5.0,https://openaccess.ihu.edu.tr/bitstreams/1290057d-3a20-435b-bd40-84db2ef866d9/download
379ee94a23d72f975e5e8368a9c6fae5415ca4d9,10.4236/jgis.2015.74029,"Spatial Modeling of Residential Crowding in Alexandria Governorate, Egypt: A Geographically Weighted Regression (GWR) Technique",2015.0,,Shawky Mansour,"Despite growing research for residential crowding effects on housing market and public health perspectives, relatively little attention has been paid to explore and model spatial patterns of residential crowding over space. This paper focuses upon analyzing the spatial relationships between residential crowding and socio-demographic variables in Alexandria neighborhoods, Egypt. Global and local geo-statistical techniques were employed within GIS-based platform to identify spatial variations of residential crowding determinates. The global ordinary least squares (OLS) model assumes homogeneity of relationships between response variable and explanatory variables across the study area. Consequently, it fails to account for heterogeneity of spatial relationships. Local model known as a geographically weighted regression (GWR) was also employed using the same response variable and explanatory variables to capture spatial non-stationary of residential crowding. A comparison of the outputs of both models indicated that OLS explained 74 percent of residential crowding variations while GWR model explained 79 percent. The GWR improvedstrength of the model and provided a better goodness of fi",5.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=58652
585bc33e1bc3e9624e91b1b35979ef185b1b45b1,10.30541/v47i4iipp.893-906,Price-setting for Residential Water: Estimation of Water Demand in Lahore,2008.0,,Tamkinat Rauf; M. W. Siddiqi,"The Water and Sewerage Agency (WASA) of Lahore is facing soaring demand and rising costs. But while massive investments are made to augment supply, tariffs remain low and are not adjusted in line with growing expenses. This has resulted not only in heavy and increasingly unsustainable reliance on loans and subsidies, but also in consumers undervaluing the resource, resulting in its inefficient utilisation. In this scenario, water tariffs badly need to be reformed. This study explores the potential of a pricing policy to regulate residential water demand in order to achieve the objectives of cost recovery, efficient water use, and equitable allocation of water resources. To this end, a demand function is estimated using household level data about water consumption and socio-economic characteristics of 156 households supplied by WASA, Lahore, for the period 2004-2006. Under block-rate tariffs the price variable is endogenously determined and a system of simultaneous equations emerges, solved here using two-stage least squares method. The estimated model explains 57 percent variation in water demand. The study finds water demand to be inelastic to price and, considering WASA’s exceedi",5.0,https://doi.org/10.30541/v47i4iipp.893-906
b35db8df7b0dd28e73bee4f666e733319810c154,10.1108/ecam-11-2023-1213,Building success: the impact of talent recruitment on the growth of the construction industry,2024.0,Engineering Construction and Architectural Management,Wei Zhang; Ning Ding; Rui Xue; Yilong Han; Chen-Yu Liu,"PurposeIn today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing research has explored the association between talent acquisition and local labor productivity or economic progress, the impact on construction growth deserves further study. This study aims to (1) explore the influence of talent recruitment on the growth of the construction industry and (2) analyze whether different regional characteristics shape the differential impact of talent acquisition on construction growth.Design/methodology/approachThis research employs a quantitative approach, focusing on 35 major cities in China. A panel data regression model is utilized to analyze annual data from 2013 to 2018, considering variables like the construction talent recruitment index, value added in construction, gross regional product per capita and others. The study also examines regional heterogeneity and conducts robustness tests to validate the findings.FindingsThe results reveal a positive and significant correlation between talent recruitment and construction industry growth. This correlation is more pronou",5.0,
68491ee58e67b617fb56891b208facc9ee1f50d4,10.3390/land14061291,Exploring the Relationship Between the Built Environment and Bike-Sharing Usage as a Feeder Mode Across Different Metro Station Types in Shenzhen,2025.0,Land,Yi-Ting Li; Jing-Wei Li; Zi-Yue Yu; Si-Ying Li; Aoyong Li,"Bike-sharing has been widely recognized for addressing the “last-mile” problem and improving commuting efficiency. While prior studies emphasize how the built environment shapes feeder trips, the effects of station types and spatial heterogeneity on bike-sharing and metro integration remain insufficiently explored. Taking the urban core area of Shenzhen as a case study, this paper examines how the built environment influences such integration during morning peak hours and how these impacts differ across station types. First, we proposed a “3Cs” (convenience, comfort, and caution) framework to capture key built environment factors. Metro stations were classified into commercial, residential, and office types via K-means clustering. Subsequently, the ordinary least squares (OLS) regression model and the multiscale geographically weighted regression (MGWR) model were employed to identify significant factors and explore the spatial heterogeneity of these effects. Results reveal that factors influencing bike-sharing–metro integration vary by station type. While land-use mix and enclosure affect bike-sharing usage across all stations, employment and intersection density are only signific",5.0,https://doi.org/10.3390/land14061291
a7eeae5be5eff85cf1b7e698d364a330b76dfb03,10.1057/s41599-023-02372-9,What are the differences in urban citizens’ preferences for the colour of condominium building facades?,2023.0,Humanities and Social Sciences Communications,Kaida Chen; Han-Liang Lin; Oliver F. Shyr; Shuying You,"The colour of a building facade is an important attribute that significantly influences consumers’ purchasing intentions towards commodity buildings. This influence is often reflected in transaction prices, allowing researchers to extract consumers’ colour preferences and analyse their psychological states. However, there is a significant lack of relevant research in this area. Therefore, this research is the first attempt to explore urban consumers’ preferences for the colour of building facades using a housing price measurement system. It also conducts comparative analyses of the consumption psychology of different homebuying consumer groups. The innovative approach in this research can serve as a reference for exploring similar issues in other regions worldwide, and the empirical results of this research hold important professional value for fields such as social psychology and urban planning. Specifically, this research takes the city of Fuzhou, China, as an example, and it uses cross-sectional transaction data from second-hand residential communities in 2020 as its research reference. Through economic and geographical research methods, this study explores consumers’ preference",5.0,https://www.nature.com/articles/s41599-023-02372-9.pdf
2912682629aefb4466757ce014b89135bfd438bd,10.3390/buildings14124082,Energy Efficiency in Subtropical Homes: Replacing Asbestos–Cement Roofs with Sustainable Alternatives,2024.0,Buildings,M. Saba; Ó. Coronado-Hernández; L. K. Gil,"This study evaluates the impact of building orientation, typology, and envelope characteristics on energy efficiency and CO2 emissions in urban dwellings in subtropical climate, with a focus on Cartagena, Colombia. North-facing dwellings consistently demonstrate superior energy performance, achieving an average efficiency increase of 4.27 ± 1.77% compared to south-facing counterparts. This trend is less pronounced near the equator due to the sun’s high zenith angle. Semi-detached homes exhibit 23.17 ± 9.83% greater energy efficiency than corner houses, attributed to reduced exterior wall exposure, which lowers energy demand and CO2 emissions by 2.16 ± 0.74 kg CO2/m2 annually. Significant disparities in emissions are observed across socioeconomic strata; homes in strata 3 and 4 show the lowest emissions (6.69 ± 1.42 kg CO2/m2 per year), while strata 5 and 6 have the highest (10.48 ± 1.42 kg CO2/m2 per year), due to differences in construction quality and glazing ratios. Roofing materials also play a key role, with thermoacoustic (TAC) roofs reducing emissions by up to 5.80% in lower strata compared to asbestos–cement roofs. Furthermore, sandwich panels demonstrate substantial potent",5.0,https://www.mdpi.com/2075-5309/14/12/4082/pdf?version=1734961631
b76b0faf831efa560e7e6d96d16e41f00bb7ecc4,10.1007/s11403-022-00370-9,Dissimilarity effects on house prices: what is the value of similar neighbours?,2022.0,Journal of Economic Interaction and Coordination,S. Bonakdar; Michael W. M. Roos,"Residential choice does not only depend on properties of the dwelling, neighbourhood amenities and affordability, but is also affected by the population composition within a neighbourhood. All these attributes are capitalised in the house price. Empirically, it is not easy to disentangle the effect of the neighbourhood on house prices from the effects of the dwelling attributes. We implement an agent-based model of an urban housing market that allows us to analyse the interaction between residential choice, population composition in a neighbourhood and house prices. Agents differ in terms of education, income and group affiliation (majority vs. minority). Whereas rich agents can afford to move to preferred places, roughly 13.01% of poor minorities and 8.02% of poor majority agents are locked in their current neighbourhood. We show that a preference to live among similar neighbours has a strong competitive effect on rich households and drives up their house prices. This is not the case with a preference for status. By introducing a policy that provides agents more access to credit, we find that all population groups denote higher satisfaction levels. Poor agents show the largest imp",5.0,https://link.springer.com/content/pdf/10.1007/s11403-022-00370-9.pdf
38e5179e651516fce504171b6e7b28d22ab659c9,10.11113/intrest.v15n1.10,Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources,2021.0,International journal of real estate studies,F. Bergadano; Roberto Bertilone; D. Paolotti; G. Ruffo,"In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM",5.0,https://intrest.utm.my/index.php/intrest/article/download/10/7
69087bd68f024aad73ece485dc00ff4f7e52e031,10.1108/jpif-05-2019-0067,International capital movement towards the Spanish real estate sector,2020.0,,Zhen-Yu Su; P. Taltavull,"The purpose of this paper is to examine the determinants that affect international capital flows (ICF) toward the Spanish real estate market over the period 1995 first quarter to 2017 fourth quarter.,VECM methodology is used to analyze time series and panel methods using pooled EGLS regression.,VECM parameter results for construction and real estate activities sectors, quickly suggesting a stable performance of capital flows toward Spanish real estate sector that the short-term fluctuation of foreign investment results contributes to the long-term equilibrium relatively soon. By applying the Monetary theory of Johnson, the model identifies a relevant role of M3 explaining capital flows to real estate, together with the lagged variables of construction and real estate activities capital flows, Spanish real interest rate and Spain’s economic growth rate; they are the significant determinants on capital movement to Spanish real estate sector. Interestingly, Spanish housing prices as an exogenous variable, directly, significantly and negatively affect real estate capital flows in all cases as a way to capture the assets price bubble.,Findings highlight reasons affecting capital flows t",5.0,https://rua.ua.es/dspace/bitstream/10045/101528/1/2019_Su_Taltavull_JPropertyInvestFinance_accepted.pdf
679e5b619ebfafaf1306e587e1f1b76ff8bde9ce,10.3390/ijgi11120615,Influence of Varied Ambient Population Distribution on Spatial Pattern of Theft from the Person: The Perspective from Activity Space,2022.0,ISPRS Int. J. Geo Inf.,Guang-Wen Song; Chunxia Zhang; Lu-Zi Xiao; Zhuoting Wang; Jian-Guo Chen; Xu Zhang,"The ambient population has been regarded as an important indicator for analyzing or predicting thefts. However, the literature has taken it as a homogenous group and seldom explored the varied impacts of different kinds of ambient populations on thefts. To fill this gap, supported by mobile phone trajectory data, this research investigated the relationship between ambient populations of different social groups and theft in a major city in China. With the control variables of motivated offenders and guardianship, spatial-lag negative binominal models were built to explore the effects of the ambient populations of different social groups on the distribution of theft. The results found that the influences of ambient populations of different social groups on the spatial distribution of theft are different. Accounting for the difference in the “risk–benefit” characteristics among different activity groups to the offenders, individuals from the migrant population are the most likely to be potential victims, followed by suburban and middle-income groups, while college, affluent, and affordable housing populations are the least likely. The local elderly population had no significant impact",5.0,https://www.mdpi.com/2220-9964/11/12/615/pdf?version=1670496396
6d25ea5c0f2537ded1b730a685568d47111d8843,10.3390/su17010251,The Nonlinear Effect of the Built Environment on Bike–Metro Transfer in Different Times and Transfer Flows Considering Spatial Dependence,2025.0,Sustainability,Yuan Zhang; Yi Meng; Xiaojian Chen; Hui-Ming Liu; Yong-Xi Gong,"Dockless bike-sharing (DBS) plays a crucial role in solving the “last-mile” problem for metro trips. However, bike–metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike–metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike–metro transfer; (2) the built environment’s impact on bike–metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike–metro transfer, with residential and employment areas displaying different",5.0,https://doi.org/10.3390/su17010251
30122d02d0675d02293f0e2c44c4da12818ecffe,10.1016/j.egypro.2014.11.977,Towards Low Carbon City Planning in a Medium Sized Low Density City,2014.0,,S. Benger,"Abstract Adelaide is a medium sized low density city located in south eastern Australia. Typical of highly urbanized Australia, where abundant land and low cost private transport contribute to urban sprawl in all major cities, Adelaide is poorly placed to move to a low carbon future, despite access to abundant renewable resources and a favourable policy environment. Australians have some of the highest emissions per capita in the world. This study examines the spatially explicit nature of CO 2 emissions across the metropolitan area and explores the relationships between emissions and a range of socio-economic variables. Contrary to findings from other westernized cities, incomes, rents and housing prices are not well correlated with distance from the city centre or train stations. Household emissions are strongly correlated with household size, rooms per household and number of motor vehicles per household. Household emissions are therefore highly associated with private car use and daily commuting distances. Public transport is poorly utilized and many urban residential areas surrounding the most heavily used public transport routes also have very high household emissions. Attitud",5.0,https://www.sciencedirect.com/science/article/pii/S1876610214028070/pdf
2afbfbf9faedd876fb4aecaa1efe644f7f511ac9,10.3390/land12020358,What Are the Relationships between Public Transit and Gentrification Progress? An Empirical Study in the New York–Northern New Jersey–Long Island Areas,2023.0,Land,Ye-Fu Chen; Hao Xi; J. Jiao,"Transit-oriented development has been a widely accepted tool among transportation planning practitioners; however, there are concerns about the risk of increasing residential property values leading to gentrification or displacements. Therefore, it is critical to provide precise investigations of the relationships between public transit and gentrification. Although numerous studies have explored this topic, few have discussed these relationships based on detailed measurements of gentrification from a regional perspective. This study aims to fill the research gap by measuring the gentrification subcategories through a hierarchical definition based on data in the New York–Northern New Jersey–Long Island areas and applying the transit desert concept as the measurement of transit services. Through multinomial logistic regression and machine-learning approaches, findings indicate that the rate of transit deserts in economically disadvantaged neighborhoods is higher than the others. In addition, the impacts of transit services are significant in gentrification but insignificant in super-gentrification. These findings can advance the knowledge of the role of the transit service in differe",5.0,https://www.mdpi.com/2073-445X/12/2/358/pdf?version=1674902194