record_id,doi,title,year,venue,authors,abstract,citations,oa_pdf 8f988a96ba5041e5f641b381ed8d1428793b511d,10.1080/14631370903090640,House price determinants in transition and EU-15 countries,2009.0,,Petra Posedel; Maruška Vizek,,48.0, 5d9163333e0fe3af8e12d7da0e3c6d3f842916b9,10.1016/j.envint.2020.105821,Who are more exposed to PM2.5 pollution: A mobile phone data approach.,2020.0,Environment International,Hua-Gui Guo; Wei-Feng Li; Fei Yao; Jiansheng Wu; Xin-Gang Zhou; Y. Yue; A. Yeh,"BACKGROUND Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales. OBJECTIVES This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales. METHODS The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects. RESULTS We found positive associatio",47.0,https://www.sciencedirect.com/science/article/pii/S0160412020317761/pdf 398fda114d9e5bcbfcf647f71fb5ebcbec8d25d8,10.2139/ssrn.3501399,"Accounting for Wealth-Inequality Dynamics: Methods, Estimates, and Simulations for France",2020.0,Journal of the European Economic Association,Bertrand Garbinti; Jonathan Goupille-Lebret; Thomas Piketty," Measuring and understanding the evolution of wealth inequality is a key challenge for researchers, policy makers, and the general public. This paper breaks new ground on this topic by presenting a new method to estimate and study wealth inequality. This method combines fiscal data with household surveys and national accounts in order to provide annual wealth distribution series, with detailed breakdowns by percentiles, age, and assets. Using the case of France as an illustration, we show that the resulting series can be used to better analyze the evolution and the determinants of wealth-inequality dynamics over the 1970–2014 period. We show that the decline in wealth inequality ends in the early 1980s, marking the beginning of a rise in the top 1% wealth share, though with significant fluctuations due largely to asset price movements. Rising inequality in savings rates coupled with highly stratified rates of returns has led to rising wealth concentration in spite of the opposing effect of house price increases. We develop a simple simulation model highlighting how changes in the combination of unequal savings rates, rates of return, and labor earnings that occurred in the early 1",47.0,https://academic.oup.com/jeea/article-pdf/19/1/620/41174629/jvaa025.pdf 549d9d4516ae5979cb086f1deb9a6931897db61f,10.1177/0003122410380868,Racial Segregation and the American Foreclosure Crisis,2010.0,American Sociological Review,Jacob S. Rugh; D. M. Massey,"The rise in subprime lending and the ensuing wave of foreclosures was partly a result of market forces that have been well-identified in the literature, but it was also a highly racialized process. We argue that residential segregation created a unique niche of minority clients who were differentially marketed risky subprime loans that were in great demand for use in mortgage-backed securities that could be sold on secondary markets. We test this argument by regressing foreclosure actions in the top 100 U.S. metropolitan areas on measures of black, Hispanic, and Asian segregation while controlling for a variety of housing market conditions, including average creditworthiness, the extent of coverage under the Community Reinvestment Act, the degree of zoning regulation, and the overall rate of subprime lending. We find that black residential dissimilarity and spatial isolation are powerful predictors of foreclosures across U.S. metropolitan areas. To isolate subprime lending as the causal mechanism through which segregation influences foreclosures, we estimate a two-stage least squares model that confirms the causal effect of black segregation on the number and rate of foreclosures a",47.0,https://www.ncbi.nlm.nih.gov/pmc/articles/4193596 3eff33e17a2ccf1deae6b3f5bb5e8eb435835f80,10.2139/ssrn.903178,There Goes the Neighborhood? Estimates of the Impact of Crime Risk on Property Values from Megan's Laws,2006.0,,Leigh L. Linden; Jonah E. Rockoff,"We combine data from the housing market with data from the North Carolina Sex Offender Registry to estimate how individuals value living in close proximity to a convicted criminal. We use the exact location of sex offenders to exploit variation in the threat of crime within small homogenous groupings of homes, and we use the timing of sex offenders' arrivals to control for baseline property values in the area. We find statistically and economically significant negative effects of sex offenders' locations that are extremely localized. Houses within a one-tenth mile area around the home of a sex offender fall by 4 percent on average (about $5,500). We also find evidence that the effect varies with distance within this range -- houses next to an offender sell for about 12 percent less while those a tenth of a mile away or more show no decline. We combine our willingness-to-pay estimates with data on sexual crimes against neighbors to estimate the costs to victims of sexual offenses. We estimate costs of over $1 million per victim -- far in excess of estimates taken from the criminal justice literature. However, we cannot reject the alternative hypotheses that individuals overestimate ",47.0,https://doi.org/10.2139/ssrn.903178 4ec5d657f725d40b7924337b63554d4c22f3b19b,10.1016/j.healthplace.2022.102924,Assessing the association between urban features and human physiological stress response using wearable sensors in different urban contexts.,2022.0,Health and Place,Zhaoxi Zhang; P. Amegbor; T. Sigsgaard; C. Sabel,"Public open space (POS) plays a significant role in fostering human health and wellbeing in cities. A major limitation of current research on POS and health is that there is little attention on the role of various urban features on people's mental health, in different urban context. This study employed wearable sensors (a wearable camera, Empatica 4 wristband and a GPS device) to measure human physiological responses to urban indicators, objectively. To do this, we selected six kinds of public open space (water area, transit area, green area, commercial area, motor traffic area and mixed office and residential area) and recruited 86 participants for an experimental study. Next, we detected urban features by using Microsoft Cognitive Services (MCS) and calculated a change score to assess human physiological stress responses based on galvanic skin response (GSR) and skin temperature from the wristband. Lastly, we applied random effect model and geographically weighted regression analysis to examine the relationship between urban indicators and human physiological stress responses. The findings show that urban flow (vehicles, bikes and people), waterbodies, greenery and places to sit ",47.0, b7f5534c7261f27e5ed58d96ff9186e1c8d34c67,10.14807/ijmp.v8i1.521,The analysis of the determinants of housing prices,2017.0,,Viktorija Cohen; Lina Karpavičiūtė,"Fundamental determinants of housing prices which affect housing demand and supply are the most common in developed countries. These are economic and financial determinants as well as demographic indicators. However, housing price analysis in less developed countries submit controversial and not sufficient results about the impact of interest rate, inflation and unemployment. Moreover, it does not investigate the influence of demographic variables and the means of economic policy. In this article the effect of GDP, unemployment, inflation, interest rate, emigration and the means of macroprudential policy on housing prices in Lithuania was evaluated. The results showed that inflation, interest rate and emigration are not causal determinants of housing prices, which mostly depend on GDP, unemployment, the means of macroprudential policy and the average housing prices in the previous period.",47.0,http://www.ijmp.jor.br/index.php/ijmp/article/download/521/616 c588888cbf7f84d6abbda8e9891246213e660f12,10.3390/su12041299,"Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China",2020.0,Sustainability,Zi-Jia Wang; Lei Cheng; Yong-Xing Li; Zhiqiang Li,"As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and r",46.0,https://doi.org/10.3390/su12041299 eae420b905d7400216716829d3bbb22999ec9bf3,10.1016/j.progress.2022.100731,"Pandemic mobility, second homes and housing market change in a rural amenity area during COVID-19 – The Brecon Beacons National Park, Wales",2022.0,Progress in Planning,N. Gallent; Phoebe Stirling; I. Hamiduddin,"The Covid-19 pandemic has been presented as a critical change driver for rural amenity areas, precipitating urban flight and a decentralisation of housing choice and investment. House prices in both near-urban and more distant rural markets saw considerable growth in 2020 and 2021, linked to a mix of second home investment, expanded demand for holiday letting, migration to exploit flexible-working opportunities, and homebuying for early retirement. These demand-side pressures, apparently accentuated by the pandemic, combine with supply-side constraints on development in rural areas (which can accommodate a limited amount of additional housing) to produce significant housing stress. However, processes affecting some areas, in different parts of the world, have arguably been generalised to all areas, and localised processes remain under-investigated. This monograph explores the experience of the Brecon Beacons National Park in Wales, UK – an amenity area with good connectivity to a number of major urban centres and therefore sources of extra-local housing demand. Focusing on second home buying, as a leading element of extra-local pressure in amenity areas, the monograph considers the",46.0,https://www.sciencedirect.com/science/article/pii/S030590062200085X/pdf 8583e9cfae17f0160faf43729d21d6d3618746d9,10.1155/2020/5287263,A Fully Automated Adjustment of Ensemble Methods in Machine Learning for Modeling Complex Real Estate Systems,2020.0,Complex,J. Alfaro-Navarro; E. Cano; Esteban Alfaro-Cortés; N. García; Matías Gámez; Beatriz Larraz,"The close relationship between collateral value and bank stability has led to a considerable need to a rapid and economical appraisal of real estate. The greater availability of information related to housing stock has prompted to the use of so-called big data and machine learning in the estimation of property prices. Although this methodology has already been applied to the real estate market to identify which variables influence dwelling prices, its use for estimating the price of properties is not so frequent. The application of this methodology has become more sophisticated over time, from applying simple methods to using the so-called ensemble methods and, while the estimation capacity has improved, it has only been applied to specific geographical areas. The main contribution of this article lies in developing an application for the entire Spanish market that fully automatically provides the best model for each municipality. Real estate property prices in 433 municipalities are estimated from a sample of 790,631 dwellings, using different ensemble methods based on decision trees such as bagging, boosting, and random forest. The results for estimating the price of dwellings sh",46.0,http://downloads.hindawi.com/journals/complexity/2020/5287263.pdf c0ba81ae547a53e25b9ac2827cb7e6b35de02c52,10.1007/s11146-019-09705-z,The Housing Cycle: What Role for Mortgage Market Development and Housing Finance?,2019.0,Journal of real estate finance and economics,L. Agnello; Vítor Castro; Ricardo M. Sousa,"We use duration analysis to assess the impact of securitization, mortgage sector liberalization and government involvement in housing finance on the length of housing booms, busts and normal times in a panel of 20 OECD countries over the period 1970Q1-2015Q4. Our results reveal that a move towards a more liberalized mortgage sector is associated with longer housing booms, while an increase in securitization is linked with shorter housing busts. They also show that the length of housing booms and busts is particularly sensitive to housing finance characteristics, but that does not seem to be the case for normal times. Additionally, government support measures do not necessarily cushion against housing busts. A careful assessment of their distributional impact, as well as their effect on the trade-off between liquidity and guarantee/loan provision, is also required to prevent (longer) housing booms. All in all, housing finance regulation may prove especially relevant to shield against the damaging effects of housing busts and the financial stability risks associated with housing booms. Monetary policy can also be an important complement to macro-prudential policies. Finally, governme",46.0,https://link.springer.com/content/pdf/10.1007/s11146-019-09705-z.pdf 7adc228f67882dd733be6fee0814971e52fc32f0,10.3390/su12020593,Neighborhood Walkability and Housing Prices: A Correlation Study,2020.0,,Eun Jung Kim; Hyun-Jung Kim,"This study aims to examine the relationship between the level of walkability and housing prices in Seoul, Korea. The average transaction price per square meter for each apartment complex was set as a dependent variable and the walkability score was used as an independent variable. This study divided a total of 5986 apartment complexes into areas with high and low housing prices for analysis. Based on the strong spatial autocorrelations of housing prices, this study employed spatial regression models in addition to the Ordinary Least Squares (OLS) model. Results showed that housing prices positively correlated with the walkability score in areas with low housing prices, whereas no significant association was observed in areas with high housing prices. Additional findings showed that housing prices were associated with building age (−), number of households in the complex (+), slope (−), and greenness (+) in both subsamples. Results also showed that high school quality had a different association with housing prices depending on the subsample (e.g., the sign was positive in areas with high housing prices and no significance in areas with low housing prices). The results herein suppor",46.0,https://www.mdpi.com/2071-1050/12/2/593/pdf?version=1579672979 8e735c69ba8da6836064605b4c3f7022c24615b9,10.1016/j.jtrangeo.2021.103065,Modeling job accessibility using online map data: An extended two-step floating catchment area method with multiple travel modes,2021.0,,Weiye Xiao; Y. Wei; Neng Wan,"Abstract Current quantitative measures of job accessibility rarely consider the interaction between job opportunities and labor force, and the effects of dynamic travel mode choice. Drawing upon multiple open-source datasets, we develop a job accessibility index by extending the two-step floating catchment area method (2SFCA). The job accessibility indices are calculated for different commuting scenarios concerning distance, time, and travel modes. The results suggest that job accessibility is very sensitive to travel modes, and using a single travel mode would contribute to a biased job accessibility index. The job accessibility indices with combined travel modes are more geographically balanced than using a single travel mode. Furthermore, the new index is employed to examine the spatial pattern of job accessibility and explore the relationship between job accessibility, housing, and population in the Pudong district, Shanghai. The new job accessibility indices manifest the impacts of ring roads on the spatial distribution of job accessibility. A comparative analysis shows that the floating population has poor driving-based job accessibility but can access job opportunities using",46.0, 799641db366806d7c5d8981404f4c311aae3252a,10.1111/1540-6229.12232,Effects of Macroeconomic Uncertainty and Labor Demand Shocks on the Housing Market,2020.0,,Johannes Strobel; B. Thanh; Gabriel Lee,"This paper shows that macroeconomic uncertainty affects the housing market in two signi?ficant ways. First, uncertainty shocks adversely affect housing prices but not the quantities that are traded. Controlling for a broad set of variables in fi?xed-effects regressions, we ?find that uncertainty shocks reduce both housing prices and median sales prices in the amount of 1.4% and 1.8%, respectively, but the effect is not statistically signi?cant for the percentage changes of all homes sold. Second, when both uncertainty and local demand shocks are introduced, the effects of uncertainty on the housing market dominate that of local labor demand shocks on housing prices, median sell prices, the share of houses selling for loss, and transactions. The aforementioned effects are largest for the states that exhibit relatively high housing price volatilities, suggesting real options effects in the housing market during the times of high uncertainty.",46.0,https://figshare.com/articles/journal_contribution/Effects_of_macroeconomic_uncertainty_and_labor_demand_shocks_on_the_housing_market/27507522 643d18ead6d0fc8b573bb89ac60c6f3e68caeb03,10.2139/ssrn.3285263,House Prices and Consumption: A New Instrumental Variables Approach,2020.0,American Economic Journal: Macroeconomics,James Graham; C. Makridis,"We introduce a novel Bartik-like instrument for house prices consisting of the local composition of housing characteristics interacted with aggregate changes in the marginal prices of these characteristics. Using household-level panel data, we estimate elasticities of nondurable consumption expenditures with respect to house prices of around 0.1. These consumption effects are concentrated among the young and those most likely to be facing tight borrowing constraints. A decomposition shows that identifying variation in the instrument is associated with times and locations where house prices have varied the most: during the housing bust of the mid-2000s and in the western United States. (JEL D12, E21, G51, R21, R31)",46.0, 56b7b85e3be0605826b7ca1296c3be5ac84f5b9d,10.1145/3339823,Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints,2019.0,ACM Trans. Spatial Algorithms Syst.,Liang Zhao; Olga Gkountouna; D. Pfoser,"Spatial regression models are widely used in numerous areas, including detecting and predicting traffic volume, air pollution, and housing prices. Unlike conventional regression models, which commonly assume independent and identical distributions among observations, existing spatial regression requires the prior knowledge of spatial dependency among the observations in different spatial locations. Such a spatial dependency is typically predefined by domain experts or heuristics. However, without sufficient consideration on the context of the specific prediction task, it is prohibitively difficult for one to pre-define the numerical values of the spatial dependency without bias. More importantly, in many situations, the existing techniques are insufficient to sense the complete connectivity and topological patterns among spatial locations (e.g., in underground water networks and human brain networks). Until now, these issues have been extremely difficult to address and little attention has been paid to the automatic optimization of spatial dependency in relation to a prediction task, due to three challenges: (1) necessity and complexity of modeling the spatial topological constrain",45.0,https://dl.acm.org/doi/pdf/10.1145/3339823 e4899911e345970c1186cd447a8b0806c54ee257,10.1016/j.jclepro.2019.118659,More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales,2020.0,Journal of Cleaner Production,S. Zuo; Shao-Qing Dai; Yin Ren,"Abstract Scientifically delineating the spatial heterogeneity of urban landscape fragmentation in relation to CO2 emissions helps the urban carbon mitigation strategy. The combination analysis across spatial resolutions, which is rare, helps explore the comprehensive relationship between urban fragmentation and CO2 emissions. This study compared the relationships between urban form fragmentation and CO2 emissions in an urban system through the analytic framework composed of the Pearson correlation analysis, geographically weighted regression (GWR), and geographical detector methods with the use of multi-source data to construct the CO2 emissions maps. As the result, there was less fragmentation with a 500-m spatial resolution (R500m) than with a 30-m spatial resolution (R30m). In terms of the GWR analysis, the coarse resolution resulted in: 1) positive coefficients of fragmentation metric becoming negative, and 2) greater absolute values of negative coefficients. As to the results of Geographical detector, single factor impact powers and interactions among fragmentation factors showed a weakening effect at R30m, but a strengthening and weakening effect at R500m. However, there were",45.0, 4867bed61e20f9da45708be5f539535b9751acc6,10.1109/tkde.2021.3112749,Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market,2020.0,IEEE Transactions on Knowledge and Data Engineering,Hao Peng; Jianxin Li; Z. Wang; Ren-Yu Yang; Ming-Sheng Liu; Ming-Ming Zhang; Philip S. Yu; Li-Fang He,"We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the To",45.0,https://arxiv.org/pdf/2008.05880 9180cc8f2daadd4570c9b29ee010874a829135c4,10.3390/su8050432,Measuring the Externality Effects of Commercial Land Use on Residential Land Value: A Case Study of Seoul,2016.0,,Hee-Jin Yang; Jihoon Song; M. Choi,"Two contrasting theories purport to explain the effects of neighborhood non-residential use on residential property values. In traditional zoning theory, separating land from commercial land use is considered to protect residential environments from negative externalities such as noise, litter, and congestion. By contrast, contemporary planning principles including Smart Growth emphasize positive impacts of mixed land use on residential environment, which lead to more walkable and sustainable communities. This study attempts to empirically investigate how positive and negative externalities of commercial land use, referred to as “proximity effects” and “disamenity effects” respectively, affect residential land values. Using data gathered in Seoul, we pay attention to two particular aspects of commercial land use: spatial concentration and neighborhood scale. Spatial concentration is determined by the number of commercial employees present in the buffer zone around an individual residential parcel. We model four geographically distinct neighborhood scales as we compare spatial concentrations in and across commercial zones. Quadratic regression analyses of our data show the trade-off",45.0,https://www.mdpi.com/2071-1050/8/5/432/pdf?version=1461994247 7f6871cd98ef39fd12e326cd778cf59c28b42165,10.3389/fpubh.2022.883177,"Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China",2022.0,Frontiers in Public Health,Lin-Chuan Yang; Bingjie Yu; Pengpeng Liang; Xianglong Tang; Ji Li,"The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This st",45.0,https://www.frontiersin.org/articles/10.3389/fpubh.2022.883177/pdf 8f3a54bf725f2ee36366af19b9ec79ac31bfe512,10.1016/j.jrurstud.2021.09.012,Rural gentrification of the ancient city of Assos (Behramkale) in Turkey,2021.0,Journal of Rural Studies,Ozgur Gocer; P. Shrestha; Didem Boyacioğlu; Kenan Gocer; Ebru Karahan,"Abstract Behramkale, also known as the ancient city of Assos, has gained considerable national and international visibility after its declaration as an Archaeological site in 1982 and later in 2000 as Urban Archaeological site. The declaration brought with it stringent regulations limiting new constructions and simple extensions in the historical village and opened new construction site outside the ancient city walls for future developments. After declaration, the village has become a major destination for cultural heritage led tourism. This largely growth oriented transformation is concerning as less attention given to shift in class structure. The paper uses the concepts of rural and tourism gentrification to understand this transformation in Behramkale using empirical evidence from a longitudinal, case study approach spanning between 2000 and 2017. The longitudinal, case study documents transformation in terms of (i) demographic and socio-economic changes; (ii) conservation practice in old settlement and development of new settlement; and (iii) change in property prices and ownership. It is seen that the authentic characteristics of the landscape and traditional buildings that i",45.0, 5fd8ed40c1d50a2576ad18cb19b11daf76c55732,10.3390/ijgi12050200,A Survey of Methods and Input Data Types for House Price Prediction,2023.0,ISPRS Int. J. Geo Inf.,Margot Geerts; S. V. Broucke; Jochen De Weerdt,"Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021 presenting a particular technique for house price prediction. Subsequently, we scrutinized these works and scored them according to model and data novelty. A cluster analysis allowed mapping of the property valuation domain and identification of trends. Although conventional methods and traditional input data remain predominant, house price prediction research is slowly adopting more advanced techniques and innovative data sources. In addition, we identify opportunities to include more advanced input data types such as unstructured data and complex spatial data and to introduce deep learning a",45.0,https://www.mdpi.com/2220-9964/12/5/200/pdf?version=1684127196 62aff1efaa5c3c853b19399c214659fef96146cd,10.5089/9781513519159.001,Effects of Monetary and Macroprudential Policies on Financial Conditions: Evidence from the United States,2015.0,Social Science Research Network,A. Zdzienicka; S. Chen; F. Kalan; Stefan Laséen; Katsiaryna Svirydzenka,"The Global Financial Crisis has reopened discussions on the role of the monetary policy in preserving financial stability. Determining whether monetary policy affects financial variables domestically -- especially compared to the effects of macroprudential policies -- and across borders, is crucial in this context. This paper looks into these issues using U.S. exogenous monetary policy shocks and macroprudential policy measures. Estimates indicate that monetary policy shocks have significant and persistent effects on financial conditions and can attenuate long-term financial instability. In contrast, the impact of macroprudential policy measures is generally more immediate but shorter-lasting. Also, while an exogenous increase in U.S. monetary policy rates tends to reduce credit and house prices in other countries -- with the effects varying with country-specific characteristics -- an increase driven by improved U.S. economic conditions tends to have the opposite effect. Finally, we do not find evidence of cross-border spillover effects associated with U.S. macroprudential policies.",45.0,https://www.elibrary.imf.org/doc/IMF001/23039-9781513519159/23039-9781513519159/Other_formats/Source_PDF/23039-9781513518725.pdf 3aee2f900a81d885cb36f5b08a2f98701871aec4,10.3390/ijgi9040227,Missing Data Imputation for Geolocation-based Price Prediction Using KNN-MCF Method,2020.0,ISPRS Int. J. Geo Inf.,Karshiev Sanjar; Olimov Bekhzod; Jaesoo Kim; Anand Paul; Jeonghong Kim,"Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the most correlated variables, and a technique to overcome the missing data. Our approach is an effective way to handle missing data in large datasets with the K-nearest neighbor algorithm based on the most correlated features (KNN–MCF). As far as we are concerned, there has been no previous research that has focused on important features dealing with missing observations. Compared to the typical machine learning prediction algorithms, the prediction accuracy of the proposed method is 92.01% with the random forest algorithm, which is more efficient than the other methods.",45.0,https://www.mdpi.com/2220-9964/9/4/227/pdf?version=1586492572 6919ce7b4d5d58f6b7d37c69d34631a442578669,10.5089/9781451871050.001.a001,Interest Rate Elasticity of Residential Housing Prices,2008.0,Social Science Research Network,P. Iossifov; M. Čihák; Amar Shanghavi,"We examine the interest rate elasticity of housing prices, advancingthe empirical literature in two directions. First, we take a commonly used cross-country panel dataset and evaluate the housing price equation using a consistent estimator in the presence of endogenous explanatory variables and a lagged dependent variable. Second, we carry-out a novel analysis of determinants of residential housing prices in a cross-section of countries. Our results show that the short-term interest rate, and hence monetary policy, has a sizable impact on residential housing prices.",45.0,https://www.elibrary.imf.org/downloadpdf/journals/001/2008/247/001.2008.issue-247-en.xml f9c1397daec1f40bc7c298e84825e4d4ff621767,10.1371/journal.pntd.0007350,"Urban villages as transfer stations for dengue fever epidemic: A case study in the Guangzhou, China",2019.0,PLoS Neglected Tropical Diseases,Hongyan Ren; Wei Wu; Tiegang Li; Zhi-Cong Yang,"Background Numerous urban villages (UVs) and frequent infectious disease outbreaks are major environmental and public health concerns in highly urbanized regions, especially in developing countries. However, the spatial and quantitative associations between UVs and infections remain little understood on a fine scale. Methodology and principal findings In this study, the relationships between reported dengue fever (DF) epidemics during 2012–2017, gross domestic product (GDP), the traffic system (road density, bus and/or subway stations), and UVs derived from high-resolution remotely sensed imagery in the central area of Guangzhou, were explored using geographically weighted regression (GWR) models based on a 1 km × 1 km grid scale. Accounting for 16.53%–18.07% of residential area and 16.84%–18.02% of population, UVs possessed 28.55%–38.24% of total reported DF cases in the core area of Guangzhou. The density of DF cases and the DF incidence rates in UVs were 1.81–3.13 and 1.82–3.06 times of that of normal construction land. Approximately 90% of the total cases were concentrated in the UVs and their buffering zones of radius ranged from 0 to 500 m. Significantly positive associations",44.0,https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0007350&type=printable 89434c7e57d5df609c72dadf64debe92c0b36794,10.1186/s12940-015-0002-2,"Polybrominated diphenyl ether serum concentrations in a Californian population of children, their parents, and older adults: an exposure assessment study",2015.0,Environmental Health,X. Wu; D. Bennett; Rebecca E. Moran; A. Sjödin; Richard S. Jones; D. Tancredi; Nicolle S. Tulve; M. S. Clifton; Maribel Colón; W. Weathers; I. Hertz-Picciotto,"BackgroundPolybrominated diphenyl ethers (PBDEs) are used as flame retardants in many household items. Given concerns over their potential adverse health effects, we identified predictors and evaluated temporal changes of PBDE serum concentrations.MethodsPBDE serum concentrations were measured in young children (2-8 years old; N = 67), parents of young children (<55 years old; N = 90), and older adults (≥55 years old; N = 59) in California, with concurrent floor wipe samples collected in participants’ homes in 2008-2009. We also measured serum concentrations one year later in a subset of children (N = 19) and parents (N = 42).ResultsPBDE serum concentrations in children were significantly higher than in adults. Floor wipe concentration is a significant predictor of serum BDE-47, 99, 100 and 154. Positive associations were observed between the intake frequency of canned meat and serum concentrations of BDE-47, 99 and 154, between canned meat entrees and BDE-154 and 209, as well as between tuna and white fish and BDE-153. The model with the floor wipe concentration and food intake frequencies explained up to 40% of the mean square prediction error of some congeners. Lower home values",44.0,https://ehjournal.biomedcentral.com/counter/pdf/10.1186/s12940-015-0002-2 7a3e4b6f3e05917df9f1088ed874523003e34d7f,10.1016/j.sbspro.2013.07.211,Attributes Influencing Housing Product Value and Price in Jakarta Metropolitan Region,2013.0,,R. A. Rahadi; S. K. Wiryono; Deddy P. Koesrindartoto; I. B. Syamwil,"Abstract This paper is a part of an ongoing dissertation research that tries to analyze the relationship between housing prices, Real Estate buyers’ preferences, Real Estate developers’ assumption of price, and external factors arising that influence the price. This paper acts as continuation study using the hypotheses from the author's previous research on the same topic to validate the hypotheses. The findings suggest that design, brand, facilities, reputation, reinvestment value, pricing policy, and speculative behavior play a significant part as internal and external attributes in price definition for housing price in Jakarta Metropolitan Region.",44.0,https://www.sciencedirect.com/science/article/pii/S187704281302106X/pdf 54f8148cde90a6670bf6b54b035fbb63e8e1421f,10.1016/j.cities.2023.104432,"Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price",2023.0,Cities,Ye-Fu Chen; Jun-Feng Jiao; A. Farahi,,44.0,https://doi.org/10.1016/j.cities.2023.104432 7a2a6868b986c9dadbb8246905ba5735a4d631b9,10.21273/hortsci.49.6.827,"Hydrophysical Properties, Moisture Retention, and Drainage Profiles of Wood and Traditional Components for Greenhouse Substrates",2014.0,,J. Fields; W. Fonteno; B. Jackson; J. Heitman; J. Owen,"Pine tree substrates (PTSs) may provide growers with sustainable substrate component options. Improved processing of PTS components has provided new materials with little scientific evaluation or understanding of their hydrophysical behavior and properties. Moisture retention characteristics were developed for two PTSs and four traditional greenhouse components: sphagnum peat, coconut coir, perlite, pine bark, shredded-pine-wood (SPW), and pine-wood-chips (PWC). Mixtures of peat containing 10%, 20%, 30%, 40%, and 50% of perlite, SPW, or PWC were also characterized. Hydrophysical properties were measured, allowing for comparison of the PTS components to the more traditional substrate components (peat, coir, perlite, and pine bark). The SPW was constructed to retain water similarly to peat and pine bark, whereas the PWC was made to increase drainage like perlite. Shredded pine wood had higher total porosity and more easily available water than did PWC components. Total porosities of SPW and PWC were similar to pine bark and coir; air space and drainage were higher than peat and coir because of the lower percentage of fine particles in the PTS components. The two PTS components had a ",43.0,https://journals.ashs.org/downloadpdf/journals/hortsci/49/6/article-p827.pdf f792e72052521e82e4bc5802dbc1bea23bb3bee9,10.22158/wjer.v7n3p1,College Graduation and Wealth Accumulation: Blacks' Diminished Returns.,2020.0,World Journal of Educational Research,S. Assari,"Background Based on the Minorities' Diminished Returns (MDRs) framework, indicators of high socioeconomic status (SES), such as high maternal educational attainment, show weaker protective effects on various developmental, behavioral, and health outcomes for Black than White families. As a result of these MDRs, families and individuals with high educational attainment still report high levels of depression, smoking, obesity, and chronic disease. Limited knowledge exists on MDRs of maternal education on indicators of wealth such as home ownership and home value. Aims Built on the MDRs framework, we tested the hypothesis of whether the effects of maternal educational attainment at birth on home ownership and home value, as proxies of wealth, vary between Black and White families. We hypothesized that: 1) high maternal education would be associated with more wealth 15 years later, and 2) compared to Whites, Blacks would be less likely to accumulate wealth (own a house) across all educational levels, given a weaker boosting effect of maternal educational attainment on wealth for Black than White families. Methods The Fragile Families and Child Well-being Study, is a 15-year follow ",43.0,http://www.scholink.org/ojs/index.php/wjer/article/download/3001/3040 7faf3a66e87f970f8ea13e235ebb5b063a5973fd,10.1186/1471-2458-6-115,"Council tax valuation bands, socio-economic status and health outcome: a cross-sectional analysis from the Caerphilly Health and Social Needs Study",2006.0,BMC Public Health,D. Fone; F. Dunstan; S. Christie; Andrew Jones; Jonathan P. West; Margaret Webber; N. Lester; J. Watkins,"Council tax valuation bands (CTVBs) are a categorisation of household property value in Great Britain. The aim of the study was to assess the CTVB as a measure of socio-economic status by comparing the strength of the associations between selected health and lifestyle outcomes and CTVBs with two measures of socio-economic status: the National Statistics Socio-Economic Classification (NS-SEC) and the 2001 UK census-based Townsend deprivation index.MethodsCross-sectional analysis of data on 12,092 respondents (adjusted response 62.7%) to the Caerphilly Health and Social Needs Study, a postal questionnaire survey undertaken in Caerphilly county borough, south-east Wales, UK. The CTVB was assigned to each individual by matching the sampling frame to the local authority council tax register. Crude and age-gender adjusted odds ratios for each category of CTVB, NS-SEC and fifth of the ward distribution of Townsend scores were estimated for smoking, poor diet, obesity, and limiting long-term illness using logistic regression. Mean mental (MCS) and physical (PCS) component summary scores of the Short-Form SF-36 health status questionnaire were estimated in general linear models.ResultsThere",43.0,https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/1471-2458-6-115 968122126d39e22143eb024980a40d546998c75e,10.3390/ijgi10020072,Evaluation of Urban Vibrancy and Its Relationship with the Economic Landscape: A Case Study of Beijing,2021.0,ISPRS Int. J. Geo Inf.,Chen Jia; Yuefeng Liu; Yun-Yan Du; Jian-Feng Huang; Teng Fei,"As one of the essential indicators for the development of a city, urban vibrancy plays an important role in evaluating the quality of urban areas and guiding urban construction. The development of spatial big data makes it possible to obtain information on user trajectories and the built environment, providing support for the evaluation of urban vibrancy. However, previous studies focused on the number of regional activities when evaluating urban vibrancy and ignored diversity, which was produced by diverse economic landscapes. In this paper, using mobile phone trajectory data, we propose a method for evaluating urban vibrancy from two dimensions: busyness and diversity, based on the improved PageRank algorithm and an index of entropy. Furthermore, in order to explore the relationship between urban vibrancy and the economic landscape, we construct an economic landscape index system based on multi-source data, including points of interest (POIs), roads, building footprints, house prices, the gross domestic product (GDP), and population data. Then, multiple linear regression is utilized to model the relationship between urban vibrancy and the urban economic landscape. The results sho",43.0,https://www.mdpi.com/2220-9964/10/2/72/pdf?version=1614085343 aed0105c242eb4fb49d98a1c421ed357f3b79532,10.22452/mjcs.vol29no1.2,Application Of Fuzzy Regression Model For Real Estate Price Prediction,2016.0,,A. G. Sarip; Muhammad Burhan Hafez; M. Daud,"Many studies have been conducted for modeling the underlying non-linear relationship between pricing attributes and price of property to forecast the housing sales prices. In recent years, more advanced non-linear modeling techniques such as Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have emerged as effective techniques to predict the house prices. In this paper, we propose a fuzzy least-squares regression-based (FLSR) model to predict the prices of real estates. A comprehensive comparison studies in terms of prediction accuracy and computational complexity of ANN, Adaptive Neuro Fuzzy Inference System (ANFIS) and FLSR has been carried out. ANN has been widely used to forecast the price of real estates for many years while ANFIS has been introduced recently. On the other hand, FLSR is comparatively new. To the best of our knowledge, no property prices prediction using FLSR was developed until recently. Besides, a detailed comparative evaluation on the performance of FLSR with other modeling approaches on property price prediction could not be found in the existing literature. Simulation results show that FLSR provides a superior prediction function as compar",43.0,https://ejournal.um.edu.my/index.php/MJCS/article/download/6889/4551 66b8fd76fb544d401cbae31ffaf67dad9d1509a4,10.1016/j.enpol.2021.112458,The Landlord-Tenant Problem and Energy Efficiency in the Residential Rental Market,2021.0,,I. Petrov; L. Ryan,"Abstract The aim of this paper is to test for the persistence of the landlord-tenant energy efficiency problem in the residential rental property market in the presence of information on property energy performance. To do this, we compare the efficiency of rental and non-rental properties using a combination of Coarsened Exact Matching (CEM) and parametric regression. We use a sample of 585,578 residential properties in the Republic of Ireland - a region that legally requires rental properties to display energy performance certificates when advertised. The findings suggest that the landlord-tenant problem is present in the Irish rental market but that it is not uniform across locations, indicating the influence of other factors. To explore this further, we exploit the regional variation in rental property prices. We find a larger difference between rental and non-rental properties’ energy efficiency in markets with scarcity in rental property supply. In addition, we are able to take advantage of a unique trait in building design to compare rental and non-rental properties which were identical at the time of their construction. The findings from this sub-group mirror our finding for",43.0,https://www.sciencedirect.com/science/article/pii/S0301421521003281/pdf c336f7e9d17cb80040edb76d5b7a9bb2969b87a6,10.2139/ssrn.3916196,Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK,2021.0,Social Science Research Network,Jean-Charles Bricongne; Baptiste Meunier; Pouget Sylvain,"While official statistics provide lagged and aggregate information on the housing market, extensive information is available publicly on real-estate websites. By web scraping them for the UK on a daily basis, this paper extracts a large database from which we build timelier and highly granular indicators. One originality of the dataset is to provide the sellers’ perspective, allowing to compute innovative indicators of the housing market such as the numb er of new posted offers or how prices fluctuate over time for existing offers. Matching selling prices in our dataset with transacted prices from the notarial database using machine learning techniques allows us to measure the negotiation margin of buyers – an innovation to the literature. During the Covid-19 crisis, these indicators demonstrate the freezing of the market and the “wait-and-see” behaviour of sellers. They also show that prices have been increasing in rural regions after the lockdown but experienced a continued decline in London.",43.0,https://doi.org/10.2139/ssrn.3916196 9f1957a4f53e85e76553df01e809e2fb62fa04bf,10.1016/s2212-5671(16)30304-5,"Influence of Macroeconomic Factors on Prices of Real Estate in Various Cultural Environments: Case of Slovenia, Greece, France, Poland and Norway",2016.0,,Bojan Grum; D. Govekar,"Abstract Based on the multiple linear regression model, we investigating which of the observed macroeconomic factors: the unemployment rate, the current account of the country stock index, gross domestic product and industrial production are significantly associated with property prices in relation to the different cultural environments: Slovenia, Greece, France, Poland and Norway. We found that there are statistically significant correlations between the prices of residential real estate and selected macroeconomic factors. The results show a distinct pattern that applies to France, Greece, Norway and Poland, where the price of real estate observed statistically significantly associated with unemployment. In the case of Slovenia, the results show that a statistically significant relationship reflects only to a share index. That is illustrated by the finding that prices in Slovenia on an annual basis, between the observed cultural environments in the observed time interval, most declined.",43.0,https://www.sciencedirect.com/science/article/pii/S2212567116303045/pdf 6a78aa3c4bc0a931d148d647fbde31ec50c438a4,10.4324/9781315708997-11,Leaving home and housing prices. The experience of Italian youth emancipation,2011.0,Youth and the Crisis,Modena Francesca; Rondinelli Concetta,"This paper provides an explanation for the postponement of youth emancipation in the Italian context mainly characterized by a sharp increase in both house and rent prices together with stagnant disposable income over the past decade. We first assemble a unique database related to the housing and rental market which is then matched with household characteristics. We find that the probability of leaving home decreases by about half percentage point and one percentage point for males and females, respectively, for a one-standard-deviation change in house prices. Together with property prices, local labour markets play a prominent role in determining decisions by unemployed youths to postpone the transition. The youngest cohort was mainly affected by the real estate market evolution that occurred in the last decade.",43.0,https://api.taylorfrancis.com/content/chapters/oa-edit/download?identifierName=doi&identifierValue=10.4324/9781315708997-11&type=chapterpdf 942e18178ee7f603abf98d9af6ddf48e0022da05,10.3390/jrfm14020047,How Does COVID-19 Affect House Prices? A Cross-City Analysis,2021.0,,Bing-Bing Wang,"Using individual level transaction data and a revised difference-in-differences method with nonparametric smoothing, we study the effect of COVID-19 on house prices. The analyses are performed on the areas of Houston, Santa Clara, Honolulu, Irvine, and Des Moines in the US, which vary in the economic features and the implementation of stay home orders. The results show that only Honolulu experienced noticeable house price declines from the outbreak, suggesting that a heavier reliance on service industries might be correlated with higher vulnerabilities. Santa Clara and Irvine lead the house price increase rates, followed by Des Moines and Houston, indicating that stronger housing market fundamentals, better amenities and less dependence on service industries are associated with more positive house price effects.",43.0,https://www.mdpi.com/1911-8074/14/2/47/pdf?version=1611578264 356a7029808f0af6955306e3f0e2f1acf7278482,10.1111/roiw.12075,"Wealth Effects, Local Area Attributes, and Economic Prospects: On the Relationship between House Prices and Mental Wellbeing",2015.0,,A. Ratcliffe,"type=""main""> This research exploits large variations in local house prices to investigate whether house prices correlate with mental wellbeing, and uses contrasting implications for the effect of house prices on the mental wellbeing of homeowners and renters to shed light on why this correlation might arise. I document a positive correlation between house prices and the mental wellbeing of both homeowners and non-homeowners, which is inconsistent with a pure wealth effect. Instead this finding suggests that local house prices provide a reflection of available amenities and economic opportunities in the area.",43.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/roiw.12075 a777fa62275d61b4c28c3e76ea5d9168d5f34331,10.1177/0042098020943482,Housing affordability sets us apart: The effect of rising housing prices on relocation behaviour,2021.0,Urban studies,Tim Winke,"Life course events such as new offspring or job loss affect a household’s demand for housing. At the same time, dynamics in the real estate market constrain where households find affordable housing. In a quasi-experimental design, this study examines the effect of increasing local housing prices on the relocation behaviour of low- and medium-income households. Difference-in-difference panel regressions using propensity score matching show that with rising local rental prices, low-income households are more likely to remain in their current housing and sustain higher levels of housing cost burden. If they move, they relocate further out of the city centre and to neighbourhoods with high unemployment rates. Rising housing markets facilitate socio-spatial segregation as middle-income households remain in economically better-off neighbourhoods. The findings highlight the additional costs of increasing housing prices in terms of the misallocation of housing and the spatial concentration of vulnerable households at the outskirts of cities.",43.0, f309cdeb8fe17983f88e290a159e1df28144de5e,10.1016/j.cities.2016.11.002,Measuring the effect of greenbelt proximity on apartment rents in Seoul,2017.0,,M. Jun; Hee-Jae Kim,,43.0, 70a4acc430b60db9f5cfa5ef12abc71b0d80edf3,10.1198/073500104000000091,Spatial Modeling of House Prices Using Normalized Distance-Weighted Sums of Stationary Processes,2004.0,,Sudipto Banerjee; A. Gelfand; John R. Knight; C. F. Sirmans,,43.0, a6d1c488b09c82140870cba8426bcd5bb60abf3a,10.1177/1078087410376614,The Spatial-Temporal Impact of Residential Foreclosures on Single-Family Residential Property Values,2011.0,,Timothy F. Kobie; Sugie Lee,,43.0, b612cfeefe79b3fb631c316ff160895751d62e9e,10.1016/j.jhe.2016.05.003,"Transit development, consumer amenities and home values: Evidence from Beijing's subway neighborhoods",2016.0,,Si-Qi Zheng; Yang Xu; Xiao-Nan Zhang; Rui Wang,,43.0, f57d211f8b473a8a61e5d84b71f6c7dc260dfd90,10.3390/su9101826,Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability,2017.0,,Yanchuan Mou; Qingsong He; Boyang Zhou,"Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on hous",42.0,https://www.mdpi.com/2071-1050/9/10/1826/pdf?version=1508128305 2681d92556888fb635fc7a8b88b6daa5218b113d,10.1177/0265813516663932,The Influence of Urban Landscape Spatial Patterns on Single-Family Property Values,2016.0,Environment and Planning B Urban Analytics and City Science,Jun-Hyun Kim; Wei Li; Galen D. Newman; Sung-Ho Kil; Sun Young Park,"Many empirical studies assessing the economic benefits of urban green space have continually documented that green space tends to increase both value and sale price of nearby residential properties. Previous studies, however, have not fully captured the quality of neighborhood level landscape spatial patterns on housing prices. To fill this literature gap, this study examined the association between landscape spatial patterns of urban green spaces and single-family home sale transactions using a spatial regression model. The research was conducted through the analysis of 11,326 housing transaction records from 2010 to 2012 in Austin, TX, USA. Variables measuring the structural, locational and neighborhood characteristics of housing were coupled with Geographic Information Systems, remote sensing and FRAGSTATS to calculate several landscape indices measuring the quality of existing landscape spatial patterns. After controlling for any spatial autocorrelation effects, we found that that larger tree and urban forest areas surrounding single-family homes positively contributed to property values, while more fragmented, isolated and irregularly shaped landscape spatial patterns resulted",42.0,https://europepmc.org/articles/pmc6191848?pdf=render 394bd6dfbf4a2fa7f8bfea34e49c5fa25acab591,10.3390/ijerph15061161,Impacts of Haze on Housing Prices: An Empirical Analysis Based on Data from Chengdu (China),2018.0,International Journal of Environmental Research and Public Health,Runqiu Liu; Chaoran Yu; Canmian Liu; Jian Jiang; Jing Xu,"Based on cross-section data of 20 districts in Chengdu, this article reviews the relationships between haze and housing prices with the combined application of Spatial Error Model (SEM) and Spatial Lag Model (SLM). The results illustrate that haze significantly have negative impacts on both the selling and rental prices of houses. Controlling other variables, if the air quality index rises by 0.1, the housing selling prices and rental prices will drop by 3.97% and 4.01%, respectively. Interestingly, housing rental prices have a more significant response to the air quality than housing sale prices. Residents are willing to pay a premium for better air quality and the influence of air quality is partially reflected in housing prices, which indicates that better air quality has been becoming a scarce resource with the improvement of people’s living standard. Furthermore, the impacts of haze on housing prices are also expected to lead to a “crowding out effect” in different regions. This would be detrimental for human capital accumulation and will accelerate the regional divergence in the internal economy and population structure, thus forming a region “fence” within cities.",42.0,https://www.mdpi.com/1660-4601/15/6/1161/pdf?version=1527937290 c88fa0cc44592135ad5aed6d68e1d50d4fefe425,10.1007/s11146-019-09712-0,Appraisal Accuracy and Automated Valuation Models in Rural Areas,2020.0,Journal of real estate finance and economics,A. Bogin; Jessica Shui,"Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.",42.0,https://link.springer.com/content/pdf/10.1007/s11146-019-09712-0.pdf 278ce18a18ba586fc5636e1f32b2397957526282,10.1108/14635789710693182,An examination of the role of Geneva and Zurich housing in Swiss institutional portfolios,1997.0,,M. Hoesli; F. Hamelink,"Swiss intitutional investors hold approximately 19% of their wealth in property, and the bulk of the allocation to property is housing. The financial reasons which are often given to motivate this investment strategy are twofold. Firstly, property returns are hypothesised to be lowly correlated with the returns on stocks and bonds, and the inclusion of property in portfolios fo financial assets should lead to diversification benefits. Secondly, property is viewed as acting as an effective hedge against inflation. The aim of this paper is to empirically investiagte these two assumptions on the basis of hedonic price indices for Geneva and Zurich apartment buildings.",42.0,https://archive-ouverte.unige.ch/unige:5930 eb66cd32411f30234897db1658100d3cf138755c,10.1177/0042098020910698,Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland,2020.0,,A. Ahrens; S. Lyons,"The classical monocentric city model suggests that property prices decrease and transport costs rise with distance to the urban centre, implying that employees face a trade-off between long commutes and high housing costs when making location decisions. Accordingly, some commuters might be forced to take on longer commutes due to rising rents in central locations. In this study, we investigate empirically whether the rental differential between employment centres and residential areas predicts changes in average commuting times. To this end, we consider a gravity model of commuting flows for Ireland over 2011–2016. We present results for Ireland and the metropolitan area of Dublin, which constitutes the largest commuting region in Ireland. The results imply that a 10% rise in rents in employment centres is associated with an up to 0.6 minute rise in one-way daily average commuting times nationally (about 2.2% of the average commute duration).",42.0, cde804babf5ddd3dcfebe29346074ab7ded5737f,10.1007/978-3-030-30967-1_9,Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction,2019.0,,Mohsen Shahhosseini; Gui-Ping Hu; Hieu Pham,,42.0,https://dr.lib.iastate.edu/bitstreams/d117954e-b1a6-4ed2-ad8d-f2bb8eed8e20/download d388466b2b927ed1ed1644529b5e2103d9f659ae,10.1007/s10618-021-00789-x,Boosting house price predictions using geo-spatial network embedding,2020.0,Data mining and knowledge discovery,Sarkar Snigdha Sarathi Das; M. E. Ali; Yuan-Fang Li; Yong-Bin Kang; T. Sellis,"Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users’ interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the geo-spatial network embedding (GSNE), that learns the embeddings of houses and various types of points of interest ",41.0,https://arxiv.org/pdf/2009.00254 199747ec923aea52036ef30630a3263acbfb5ff2,10.3390/su10041298,"Walled Buildings, Sustainability, and Housing Prices: An Artificial Neural Network Approach",2018.0,,R. Li; Kai-Yin Cheng; M. Shoaib,"Various researchers have explored the adverse effects of walled buildings on human health. However, few of them have examined the relationship between walled buildings and private housing estates in Hong Kong. This study endeavors to fill the research gap by exploring the connections among walled-building effects, housing features, macroeconomic factors, and housing prices in private housing estates. Specifically, it reveals the relationship between walled buildings and housing prices. Eight privately owned housing estates are selected with a total of 11,365 observations. Results are analyzed to study the factors that affect the housing price. Firstly, unit root tests are carried out to evaluate if the time series variables follow the unit root process. Secondly, the relationship between walled buildings and housing price is examined by conducting an artificial neural network. We assumed that the housing price reduces due to walled-building effects, given that previous literature showed that heat island effect, and blockage of natural light and views, are common in walled-building districts. Moreover, we assume that housing price can also be affected by macroeconomic factors and ho",41.0,https://www.mdpi.com/2071-1050/10/4/1298/pdf?version=1525349533 29644041528b882e5af572a33c65e92524314e1d,10.1561/105.00000083,"Expenditure Cascades, Low Interest Rates or Property Booms? Determinants of Household Debt in OECD Countries",2018.0,Review of Behavioral Economics,Engelbert Stockhammer; Rafael Wildauer,"The past decades have witnessed a strong increase in household debt and high growth of private consumption expenditures in many countries. This paper empirically investigates four explanations: First, the expenditure cascades hypothesis argues that an increase in inequality induced lower income groups to copy the spending behaviour of richer peer groups and thereby drove them into debt (‘keeping up with the Joneses’). Second, the housing boom hypothesis argues that increasing property prices encourage household spending and household borrowing due to wealth effects, eased credit constraints and the prospect of future capital gains. Third, the low interest hypothesis argues that low interest rates encouraged households to take on more debt. Fourth, the financial deregulation hypothesis argues that deregulation of the financial sector boosted credit supply. The paper tests these hypotheses by estimating the determinants of household borrowing using a panel of 11 OECD countries (1980-2011). Results indicate that real estate prices and low interest rates were the most important drivers of household debt. In contrast the data does not support the expenditure cascades hypothesis as a gen",41.0,http://gala.gre.ac.uk/id/eprint/20882/7/20882%20WILDAUER_Expenditure_Cascades%2C_Low_Interest%20Rates_Credit_Deregulation_or_Property_booms%202018.pdf 9b9f9b81641cca03cd602ed12b5bb79c297f06f7,10.1016/j.habitatint.2019.102068,"Temporal and spatial effects of a ‘Shan Shui’ landscape on housing price: A case study of Chongqing, China",2019.0,,Gui-Wen Liu; Xizi Wang; Jian-Ping Gu; Yong Liu; Tao Zhou,"Abstract A residential environment with a ‘Shan Shui’ landscape (river and mountain landscapes) not only has high aesthetic value but also indicates good fortune in traditional Chinese geomantic omen theory. As determined at the 19th National Congress, the national strategic goal of China is to achieve green development and ecological construction for urbanization. To comprehensively recognize the economic value of a ‘Shan Shui’ landscape is a win-win solution for real estate developers, as well as for the sustainable urbanization of China. Most of the literature related to the amenity value of urban landscapes has focused on the value of individual landscapes while ignoring the interactions between landscapes, leading to controversy around the amenity value of the landscape as determined in different empirical case studies. This study takes the ‘city of Shan Shui’, Chongqing, as the case study to explore the interactions of river and mountain landscapes and the temporal and spatial variances in the value of ‘Shan Shui’. The findings suggest that access to the ‘Shan Shui’ landscapes of neighboring houses would increase the price of a house by 15.01%, which is much higher than the v",41.0, 622982414b5dd59d6b00532ca485f618ccc962ad,10.1016/j.landurbplan.2017.08.012,Is social polarization related to urban density? Evidence from the Italian housing market,2017.0,Landscape and Urban Planning,V. Antoniucci; G. Marella,"Abstract The aim of this work is to assess variations in housing market polarization in Italian cities since 2008, during the years of economic recession in Italy. An index of market polarization was constructed and correlated with urban density and socio-economic factors, as proxies for urban development of the Southern Euro countries. The hypothesis is that social polarization has increased since 2008, when an economic and sovereign debt crisis began, and is negatively correlated with housing market trends in more densely populated cities than in sprawl territories. Multivariate regression on housing price variations since 2008, with a dataset of economic variables concerning 112 Italian provincial capitals, was used to verify the hypothesis, and analysis confirmed that housing market polarization had increased since 2008 and shows significant statistical correlations with urban density, socio-economic characteristics, and housing affordability. Empirical evidence also demonstrates the close relationship among price trends, urban density, and the socio–economic structure of urban populations.",41.0,https://www.research.unipd.it/bitstream/11577/3241766/4/Landscape.pdf 6c2a39f89e7a64d98535f889debdc2e2b819357c,10.2139/ssrn.3899313,The 2000s Housing Cycle With 2020 Hindsight: A Neo-Kindlebergerian View,2021.0,Social Science Research Network,G. Chodorow-reich; Adam M. Guren; Timothy J McQuade," With “2020 hindsight,” the 2000s housing cycle is not a boom-bust but a boom-bustrebound. Using a spatial equilibrium regression in which house prices are determined by income, amenities, urbanization, and supply, we show that long-run city-level fundamentals predict not only 1997-2019 price and rent growth but also the amplitude of the boom-bust-rebound. This evidence motivates our model of a cycle rooted in fundamentals. Households learn about fundamentals by observing “dividends” but become over-optimistic in the boom due to diagnostic expectations. A bust ensues when beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom-bust-rebound with a single shock and accounts quantitatively for the dynamics of prices, rents, and foreclosures in cities with the largest cycles. We draw implications for asset cycles more generally.",41.0,https://doi.org/10.2139/ssrn.3899313 a30fd51dbaa3270aa960796ead177febad313dee,10.1109/dsaa.2018.00043,The Economic Value of Neighborhoods: Predicting Real Estate Prices from the Urban Environment,2018.0,International Conference on Data Science and Advanced Analytics,Marco De Nadai; B. Lepri,"Housing costs have a significant impact on individuals, families, businesses, and governments. Recently, online companies such as Zillow have developed proprietary systems that provide automated estimates of housing prices without the immediate need of professional appraisers. Yet, our understanding of what drives the value of houses is very limited. In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception. We also develop and release a framework able to now-cast housing prices from Open data, without the need for historical transactions. Experiments involving 70,000 houses in 8 Italian cities highlight that the neighborhood's vitality and walkability seem to drive more than 20% of the housing value. Moreover, the use of this information improves the nowcast by 60%. Hence, the use of property's surroundings' characteristics can be an invaluable resource to appraise the economic and social value of houses after neighborhood changes and, potentially, anticipate gentrification.",41.0, 67e320647f1b505521315f69e12630d04bd3462a,10.1016/j.scs.2021.102769,"Spatial analysis of urban smart growth and its effects on housing price: The case of Isfahan, Iran",2021.0,,Bagher Bagheri; Raana Shaykh-Baygloo,"Abstract Because of its multifaceted approach and its emphasis on promoting healthy and sustainable development, smart growth has become one of the popular urban growth policies. The present study conceptualizes the smart growth by a set of principles - including human scale, mixed land use, compact development, environmental health, and balanced distribution of services - and examines the status and spatial variation of these principles and as well, housing attributes and socio-economic characteristics in the Isfahan metropolitan area, at neighborhood level. The association between smart growth and housing price is also investigated. The analyses are carried out by applying principal component analysis (PCA), ordinary least squares (OLS) regression, and geographically weighted regression (GWR). Based on both OLS and GWR models, the overall index of urban smart growth positively influences housing prices, albeit the effect size is different in various neighborhoods of the city.",41.0, ee1e8ef02d8a9db9761d949199796b9035385356,10.1016/j.tourman.2020.104134,What caused the rise of Airbnb? An examination of key macroeconomic factors,2020.0,,T. Dogru; Ying-Sha Zhang; Courtney Suess; M. Mody; Umit Bulut; E. Sirakaya-Turk,"Abstract The social and economic implications of the Airbnb phenomenon have been the subject of much research. Yet, the academic literature on Airbnb is nascent. Specifically, the issue of whether major macroeconomic conditions affect the supply of Airbnb has not been investigated. To address this gap, we propose a conceptual model that explains the determinants of Airbnb supply and examine the extent to which major macroeconomic factors affect the supply of Airbnb. Specifically, we analyze the effects of hotel room rates (ADR), hotel demand, tourism demand, house prices, gross domestic product (GDP), wages and unemployment on the supply of Airbnb in 50 U.S. states. Results show that increases in hotel ADR, house prices, and GDP have contributed to an increase in the supply of Airbnb, whereas increases in unemployment rates and wages have adverse effects on Airbnb supply. Theoretical and policy implications are discussed within realms of macroeconomic theory.",41.0, fe907903fde6022c475a2edf5333c983301c6180,10.1007/s11146-009-9176-0,The Capitalization of Building Codes in House Prices,2011.0,,Randy E. Dumm; G. Sirmans; Greg T. Smersh,,41.0,https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=1666280 3c2be388a3fe29f4a84005de491a9ea6f5d23109,10.1007/s11146-023-09944-1,Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach,2023.0,Journal of real estate finance and economics,Juergen Deppner; Benedict von Ahlefeldt-Dehn; Eli Beracha; Wolfgang Schaefers,"In this article, we examine the accuracy and bias of market valuations in the U.S. commercial real estate sector using properties included in the NCREIF Property Index (NPI) between 1997 and 2021 and assess the potential of machine learning algorithms (i.e., boosting trees) to shrink the deviations between market values and subsequent transaction prices. Under consideration of 50 covariates, we find that these deviations exhibit structured variation that boosting trees can capture and further explain, thereby increasing appraisal accuracy and eliminating structural bias. The understanding of the models is greatest for apartments and industrial properties, followed by office and retail buildings. This study is the first in the literature to extend the application of machine learning in the context of property pricing and valuation from residential use types and commercial multifamily to office, retail, and industrial assets. In addition, this article contributes to the existing literature by providing an indication of the room for improvement in state-of-the-art valuation practices in the U.S. commercial real estate sector that can be exploited by using the guidance of supervised ma",40.0,https://link.springer.com/content/pdf/10.1007/s11146-023-09944-1.pdf c6d63ae6c71aefb33c97c1865c6d5a54c44d1a1e,10.1016/j.regsciurbeco.2016.04.001,House prices at different stages of the buying/selling process ☆,2016.0,,C. Shimizu; K. Nishimura; Tsutomu Watanabe,"In constructing a housing price index, one has to make at least two important choices. The first is the choice among alternative estimation methods. The second is the choice among different data sources of house prices. The choice of the dataset has been regarded as critically important from a practical viewpoint, but has not been discussed much in the literature. This study seeks to fill this gap by comparing the distributions of prices collected at different stages of the house buying/selling process, including (1) asking prices at which properties are initially listed in a magazine, (2) asking prices when an offer for a property is eventually made and the listing is removed from the magazine, (3) contract prices reported by realtors after mortgage approval, and (4) registry prices. These four prices are collected by different parties and recorded in different datasets. We find that there exist substantial differences between the distributions of the four prices, as well as between the distributions of house attributes. However, once quality differences are controlled for, only small differences remain between the different house price distributions. This suggests that prices col",40.0,https://hit-u.repo.nii.ac.jp/record/2054795/files/IFD_WP69.pdf 9ac56437c1f98c3686ed8750a1c74cf7d7d40735,10.1016/j.trd.2022.103285,Investigating factors influencing takeout shopping demand under COVID-19: Generalized additive mixed models,2022.0,Transportation Research Part D: Transport and Environment,Fan Zhang; Yanjie Ji; Hui-Tao Lv; Xinwei Ma; Chen Kuai; Wen-Hao Li,"The COVID-19 pandemic severely hampered the freedom of shopping travel while increasing individuals’ interest in takeout. Although many studies have examined takeout shopping, the available literature provides insufficient evidence on the factors influencing takeout shopping demand under the COVID-19. In this study, generalized additive mixed models were developed based on sampling data of takeout orders in Nanjing before, during, and post the pandemic to measure the associations between takeout shopping demand and neighborhood characteristics at the business circle scale. The results show that population density, house prices, road density, and catering all have a significant impact on takeout shopping demand, while the roles of land use (residential and company indexes) before and post the pandemic are opposite. Besides, the factors influencing the recovery of the demand before and after the pandemic were analyzed. These findings provide important insights into the development of the takeout industry in the post-pandemic era.",40.0,https://doi.org/10.1016/j.trd.2022.103285 78e925a77ed913667329cb67952abbcb968fc220,10.1016/j.cities.2020.102878,Breaking the land monopoly: Can collective land reform alleviate the housing shortage in China's mega-cities?,2020.0,,L. Tian; Ya-Qi Yan; G. Lin; Ya-Xin Wu; Lei Shao,"Abstract This paper engages with ongoing theoretical enquiries into the dynamism of housing shortage in many global cities. The research critically evaluates the recent Chinese practice of increasing the supply of the collectively owned land as a means to alleviate housing shortage in cities. Taking the four first-tier cities, Beijing, Shanghai, Guangzhou and Shenzhen as case studies, this paper analyzes their characteristics of land supply and housing price/rent change, and argues that increasing land supply through encouraging rental housing to be built upon collectively owned land did not contribute in any significant way the alleviation of housing shortage in China's megacities because of the manipulation and circumvention of the local governments that held strong vested interests in urban land monopoly. Findings of this research contribute new theoretical insights into the global dynamism of housing developments and call for special attention paid to the important effects of the land disposition regimes upon housing supply, the sophisticated central-local power relations internal to the state, and the spatiality of housing developments as well as their geographical contingency",40.0, 50915bab05b8c9a1e33ae813b9cd868e78344368,10.19139/soic.v7i1.435,"Predicting Home Value in California, United States via Machine Learning Modeling",2019.0,"Statistics, Optimization and Information Computing",Yitong Huang,"The market value of real estate is difficult to predict with simple regression model due to the diversity and complexity of the real data. In this paper, with the latest real estate data of three counties in Los Angeles, California, United States, both linear and non-linear machine learning methods are employed to predict the log error of the home value. The motivation is to improve the accuracy in home value prediction with advanced methods. The main contribution is that it finds that traditional linear models are not predictive for complex home value data sets, while tree based non-linear models are most accurate with the lowest mean square errors.",40.0,http://www.iapress.org/index.php/soic/article/download/2019-M-5/403 3206ef12965a9c5c68b888e9b47a984d9c9c81f0,10.6004/jnccn.2023.7023,Social Determinants of Health and Racial Disparities in Cardiac Events in Breast Cancer.,2023.0,The Journal of the National Comprehensive Cancer Network,Nickolas Stabellini; Mantas Dmukauskas; M. Bittencourt; Jennifer Cullen; Amie J. Barda; J. Moore; Susan F. Dent; H. Abdel-Qadir; A. Kawatkar; A. Pandey; J. Shanahan; J. Barnholtz-Sloan; K. Waite; A. Montero; A. Guha,"BACKGROUND Racial disparities have been reported for breast cancer and cardiovascular disease (CVD) outcomes. The determinants of racial disparities in CVD outcomes are not yet fully understood. We aimed to examine the impact of individual and neighborhood-level social determinants of health (SDOH) on the racial disparities in major adverse cardiovascular events (MACE; consisting of heart failure, acute coronary syndrome, atrial fibrillation, and ischemic stroke) among female patients with breast cancer. METHODS This 10-year longitudinal retrospective study was based on a cancer informatics platform with electronic medical record supplementation. We included women aged ≥18 years diagnosed with breast cancer. SDOH were obtained from LexisNexis, and consisted of the domains of social and community context, neighborhood and built environment, education access and quality, and economic stability. Race-agnostic (overall data with race as a feature) and race-specific machine learning models were developed to account for and rank the SDOH impact in 2-year MACE. RESULTS We included 4,309 patients (765 non-Hispanic Black [NHB]; 3,321 non-Hispanic white). In the race-agnostic model (C-in",39.0,https://jnccn.org/downloadpdf/journals/jnccn/21/7/article-p705.pdf eff9f887fe42af028d133a463e98f26b95e6ce24,10.1016/j.jmoneco.2022.11.010,Wealth Inequality Dynamics in Europe and the United States: Understanding the Determinants,2022.0,Journal of Monetary Economics,Thomas Blanchet; C. Martínez-Toledano,"This paper studies the interaction between the long-term dynamics of aggregate household wealth and the wealth distribution in Europe and the United States. We do so by building the first Distributional Wealth Accounts for Europe, including households’ assets, liabilities, investment flows, and the wealth distribution for most European countries from 1970-2020. We find that although aggregate household wealth to income ratios have followed a similar increasing pattern in both Europe and the United States since 1970, wealth concentration has increased much faster in the United States. Using standard wealth accumulation decompositions and counterfactual simulations, we show that the weaker rise in labor income inequality and the stronger rise in house prices relative to stock market prices in Europe versus the United States seem to explain why Europe has experienced a more moderate rise in wealth concentration since the mid-1980s. ∗We thank Thomas Piketty for useful comments and suggestions and participants at the Second World Inequality Conference, the First London Inequality Workshop, the Spanish Economic Association Annual Meeting and seminars at Universitat Autónoma de Barcelona ",39.0,http://hdl.handle.net/10044/1/101928 eca0fef58bf00abf31c95fbd659858a27af78ef0,10.1016/j.tbs.2020.11.004,China’s millennial car travelers’ mode shift responses under congestion pricing and reward policies: A case study in Beijing,2021.0,,Yun-Tao Guo; Ya-Ping Li; Panagiotis Ch. Anastasopoulos; S. Peeta; Jian Lu,"Abstract This paper investigates the potential impacts of congestion pricing and reward policies on migrant and resident millennial car travelers’ morning commute mode shift responses in China. A stated-preference survey developed for this study was conducted in 2017 among millennial car travelers living or working in Beijing’s inner district, from which approximately 2000 responses were collected. Separate random parameters bivariate ordered probit models were estimated for migrant and resident millennial car travelers to capture the differences between their mode shift responses, unobserved heterogeneity, and correlation between their mode shift responses to congestion pricing and reward policies. Sociodemographic characteristics, travel behavior and needs, residential location accessibility to and by transit, attitudes towards travel and congestion pricing and reward policies were found to affect millennial car travelers’ mode shift responses. In addition, the impacts of the contributing factors on the mode shift responses were very different between the congestion pricing and reward policies and between the migrant and resident millennial car travelers. The findings from the mo",39.0, b7f971137edeb2ec0b4f5c5205affd4d8bf0debf,10.1002/psp.2087,"Parents, local house prices, and leaving home in Britain",2018.0,,Sait Bayrakdar; Rory Coulter,"Leaving the parental home is a significant step in young adults' housing careers and pathways to independence. Although a large literature examines how life course trajectories influence leaving home, much less is known about how the ""linked lives"" of parents and the local cost of housing shape young people's departures from the family residence. By enriching the U.K. Household Longitudinal Study with house price data, this study investigates how parental attributes and the geography of local housing costs influence home leaving in contemporary Britain. The results show that higher local house prices are associated with delayed departure from the parental home, although the relative magnitude of this effect is modest. By contrast, the effects of parental factors are more nuanced. Parental characteristics have little impact on the odds of leaving home to form partnerships, whereas the likelihood of departing to live alone or in shared accommodation is reduced by parental homeownership or living with both biological parents. Taken together, these findings suggest that young adults' residential pathways are shaped by the complex patterns of choice and constraint that are generated by ",39.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp.2087 f6b71453d144e8c46e5cbfb0d358a992e0ddcc26,10.1016/j.rser.2021.111675,Modelling potential visibility of wind turbines: A geospatial approach for planning and impact mitigation,2021.0,Renewable & Sustainable Energy Reviews,H. Alphan,"Abstract There has been a dramatic increase in wind power supply due to the growing demand for renewable energy sources across the globe. In this process, technological advancements have led to the emergence of more sophisticated wind turbines with higher hubs and larger swept areas to reduce the unit costs for power generation. However, there is also a growing concern about the visibility impacts of wind turbines. Spatial modelling of the potential visibility of wind turbines holds strategic information for wind power siting decisions. The visibility information is critical to breaking trade-offs between energy production and protecting the visual amenity of landscapes. Currently, public attention to the visual impacts of wind turbines tends to rise in countries with high wind power capacities. Decreases in property values due to wind turbine visibility and negative impacts on scenery are among the factors that draw public attention. These implications make it necessary to use spatial analysis and develop mitigation strategies based on the geospatial outputs. This paper aims to produce Potential Visibility Models (PVM) to analyse wind turbine visibility from candidate observation ",39.0, 3b44f43a3e57bd2a6158ac3aee2966152153ca0a,10.1016/j.habitatint.2018.01.001,How do housing price and sentiment affect consumption distribution in China?,2018.0,Habitat International,E. Hui; Zhaoyingzi Dong; Sheng-Hua Jia,"Abstract Considering the lack of exploration of housing market sentiment in previous work, this paper uses quantile regression for panel data (QRPD) to investigate how housing price and housing market sentiment affect non-housing consumption distributions among owners and renters during their life cycles in China. The results show that the positive effect of housing price on consumption is stronger at the higher and lower parts of the distribution, and the differences caused by ages are more significant for owners. Furthermore, the housing market sentiment plays a significant role in owners' and highest-consuming renters' consumption. The heterogeneities in QRPD results suggest that the least square method provides less information. This study offers practical implication for governments in conducting different housing policy strategies for different households so as to help households benefit from the development of housing market and hence increase social welfare and equality.",39.0,http://hdl.handle.net/10397/78344 312899c666f19fe1adbdbdbbbb9ae92d41b24a1a,10.1920/wp.cem.2020.520,"THE HOUSING STOCK, HOUSING PRICES, AND USER COSTS: THE ROLES OF LOCATION, STRUCTURE, AND UNOBSERVED QUALITY",2015.0,International Economic Review,F. Oswald; L. Nesheim; Jonathan Halket,"Using the English Housing Survey, we estimate a supply side selection model of the allocation of properties to the owner-occupied and rental sectors. We find that location, structure and unobserved quality are important for understanding housing prices, rents and selection. Structural characteristics and unobserved quality are important for selection. Location is not. Accounting for selection is important for estimates of rent-to-price ratios and can explain some puzzling correlations between rent-to-price ratios and homeownership rates. We interpret this as strong evidence in favor of contracting frictions in the rental market likely related to housing maintenance.",39.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/iere.12475 95ed7cf5b17bda8e6fed7d46057d17eb49f6b738,10.1109/iccsnt47585.2019.8962443,House Price Prediction Approach based on Deep Learning and ARIMA Model,2019.0,International Conference on Computer Science and Network Technology,Feng Wang; Yang Zou; Haoyu Zhang; Haodong Shi,"The nonlinear relationship between influential factors and house price and inadequate number of sample size could be the cause of the poor performance of the traditional models. Meanwhile, the daily data of the real estate market is very huge and it is increasing rapidly. The traditional house price prediction approaches are lack of capacity for massive data analysis, causing low utilization of data. To address these concerns, a house price prediction model based on deep learning is proposed in this paper, implemented on the TensorFlow framework. Adam optimizer is used to train the model, where Relu function is adopted to be the activation function. Then house price trend is predicted based on the ARIMA model. By using Scrapy, housing data are obtained from Internet to be the experimental dataset. Comparative experiments were conducted between the proposed approach and SVR method. The experimental results show that individual house price predicted by the proposed approach is better than that of SVR method. And the predicted house price trend is mainly agreement with the real situation.",39.0, f2308f0a4f0981801b518b9ca2152bcb4c797ad7,10.1109/iccubea.2018.8697639,Real Estate Value Prediction Using Linear Regression,2018.0,International Conference on Computing Communication Control and automation,Nehal Ghosalkar; Sudhir N. Dhage,"The real estate market is a standout amongst the most focused regarding pricing and keeps fluctuating. It is one of the prime fields to apply the ideas of machine learning on how to enhance and foresee the costs with high accuracy. There are three factors that influence the price of a house which includes physical conditions, concepts and location. The current framework includes estimating the price of houses without any expectations of market prices and cost increment. The objective of the paper is prediction of residential prices for the customers considering their financial plans and needs. By breaking down past market patterns and value ranges, and coming advancements future costs will be anticipated. This examination means to predict house prices in Mumbai city with Linear Regression. It will help clients to put resources into a bequest without moving toward a broker. The result from this research proved linear regression gives minimum prediction error which is 0.3713.",39.0, ea46946c60f76c3ba16491c18fdd39725e4d37b9,10.1016/j.regsciurbeco.2019.103470,Tests for spatial dependence and heterogeneity in spatially autoregressive varying coefficient models with application to Boston house price analysis,2019.0,Regional Science and Urban Economics,Dengqiu Li; Chang-Lin Mei; Ning Wang,"Abstract Spatially autoregressive varying coefficient models are a powerful tool for simultaneously dealing with spatial dependence and spatial heterogeneity in spatial data analysis. Different methods have been developed for estimating the models. Nevertheless, little work has been devoted to their statistical inference issues. In this paper, two generalized-likelihood-ratio-statistic-based bootstrap tests are developed to detect spatial autocorrelation in the response variable and to identify constant coefficients in the regression functions, respectively. The simulation studies show that both tests are of accurate size and satisfactory power. The Boston house price data are finally analyzed to demonstrate the application of the proposed tests in the detection of spatial dependence and heterogeneity.",39.0, 91a44e2cd5a021b353ef15f6637207fbc3662ee4,10.1108/17538271111153022,The impact of proximity to cell phone towers on residential property values,2011.0,,O. Filippova; Michael Rehm,,39.0, b3cb36005e0f8ac884bd93f9f15c504fe217b252,10.1016/j.econmod.2014.07.018,The impact of the 2008 financial crisis on housing prices in China and Taiwan: A quantile regression analysis,2014.0,,Hsin-Hong Kang; Shu-Bing Liu,,39.0, 5273268598b5133424e963e0c43f622b719595ae,10.3390/rs16061107,Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich,2024.0,Remote Sensing,Chao Gao; Sha-Sha Li; Maopeng Sun; Xi-Yang Zhao; Dewen Liu,"Urbanization has profoundly reshaped the patterns and forms of modern urban landscapes. Understanding how urban transportation and mobility are affected by spatial planning is vital. Urban vibrancy, as a crucial metric for monitoring urban development, contributes to data-driven planning and sustainable growth. However, empirical studies on the relationship between urban vibrancy and the built environment in European cities remain limited, lacking consensus on the contribution of the built environment. This study employs Munich as a case study, utilizing night-time light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy while constructing a comprehensive assessment framework. Firstly, the spatial distribution patterns and spatial correlation of various types of urban vibrancy are revealed. Concurrently, based on the 5Ds built environment indicator system, the multi-dimensional influence on urban vibrancy is investigated. Subsequently, the Geodetector model explores the heterogeneity between built environment indicators and comprehensive vibrancy along with its economic, social, cultural, and environmental dimensions",38.0,https://www.mdpi.com/2072-4292/16/6/1107/pdf?version=1711080434 549c231d20f6e6cb5280caa92385e85bdd825716,10.1108/ijhma-01-2019-0009,An analysis on housing affordability in Malaysian housing markets and the home buyers’ preference,2019.0,International Journal of Housing Markets and Analysis,Amy Soon; Consilz Tan," Purpose This paper aims to investigate the housing preference and housing affordability in Malaysian housing markets. There is a lack of research on the gap between supply and demand of houses in this market. Urbanization has increased the demand of houses in urban areas. However, the high demand in residential units increases the housing price which causes the affordability level dropped. Besides, the residences that provided by developers do not meet the expectation of the home buyers. There are three attributes that examined in this research to understand the home buyers’ preference. Design/methodology/approach This paper provides quantitative analysis on the housing affordability and the home buyers’ preference. This paper presents the results on the home buyers’ housing affordability and buying preference on houses. In addition, the study further confirmed the significant relationship between monthly income and type of preferred house, as well as monthly income and range of housing affordability using cross-tabulation analysis. Findings The findings indicated that the housing price in the current market is not affordable by most of the homebuyers and there are certain att",38.0, ac95e2bb667cd4a9dcfef66f355699ca3bd58619,10.1016/j.jclepro.2018.06.205,"Spatial differentiation of daily travel carbon emissions in small- and medium-sized cities: An empirical study in Kaifeng, China",2018.0,Journal of Cleaner Production,Pei-Jun Rong; Li-Jun Zhang; Yaochen Qin; Zhi-Xiang Xie; Ya-Nan Li,"Abstract Issues relating to low-carbon transportation have received increasing attention in recent years for two competing reasons: the effort to slow down global warming and the upward trend in private vehicle ownership in China. Residential carbon emissions and the mechanisms influencing them have been adequately addressed by the published literature. However, both the built environment and socio-economic factors are spatially heterogeneous within cities, and this can be expected to lead to a corresponding spatial heterogeneity in residential carbon emissions. To better characterize spatial heterogeneity in the mechanisms influencing carbon emissions, this study collected fine-scale spatially-distributed data pertaining to carbon emissions arising from the travel activities of residents in Kaifeng City. The spatial characteristics of and factors influencing household travel carbon emissions were then investigated using spatial auto-correlation analysis and the geographical weighted regression (GWR) method. The results show that areas with high values of household travel carbon emissions were primarily in the new urban development zone and in the more rapidly-expanding built-up ar",38.0, 7b79cc1aa1185453819fef6a04079e11b523c41e,10.3390/ijerph18199987,"Effects of Land Use on Land Surface Temperature: A Case Study of Wuhan, China",2021.0,International Journal of Environmental Research and Public Health,Youpeng Lu; W. Yue; Ya-Ping Huang,"In this study, we aim to understand the impact of land use on the urban heat island (UHI) effect across an urban area. Considering the case study of Wuhan, China, land use factors and land surface temperatures (LSTs) of 589 planning management units were quantified in order to identify the spatial autocorrelation of LST, which indicated that a traditional regression would be invalid. By investigating the relationships between land use factors and the LST in summer, based on spatial regression models including the spatial lag model and the spatial error model, four conclusions were derived. First, the spatial error model effectively explains the relationships between LST and land use factors. Second, the impact on LST of the percentage of industrial areas is significant even though the impacts of land cover and building-group morphology indicators are combined, indicating that anthropogenic heat emission of industrial production contributes to high LSTs. Third, the relationship between the percentage of commercial area and LST is significant in the Pearson correlation analysis and traditional regression models, while not significant in spatial error model, suggesting that the urban ",38.0,https://www.mdpi.com/1660-4601/18/19/9987/pdf?version=1632462635 2463b67abe7ac7dc9d0d4342b36d71bf6498f365,10.1177/0361198120924630,How Built Environment Impacts Online Car-Hailing Ridership,2020.0,,Hui Bi; Zhirui Ye; Chao Wang; En-Hui Chen; Yiwu Li; Xiao-Hui Shao,"Extensive research has shown that unilateral optimization of transit systems is not effective enough to significantly increase its transport efficiency. Considering that urban land-use characteristics, including residential, work, consumption, transit, and so forth, are significantly interrelated with travel demands and travel behaviors, this paper provides a way to optimize transit system by raising awareness of the relation between ridership and built environment. This paper adopted point of interest (POI) data to investigate the effect of physical built environment on online car-hailing ridership in Chengdu, China. The study area was tessellated with several Voronoi cells; these cells were further clustered into three ridership patterns based on the time-varying characteristic of ridership. Given that some differences existed in the three ridership patterns, a separate spatial ridership model was developed to understand the factors that influence ridership patterns using geographic weighted regression (GWR) analysis. The data and results verified that the built environment had various influences on online car-hailing alighting ridership in spatial and temporal dimensions, of whi",38.0, 865fcba893598d78106e2540974f1e301b3d2c75,10.1145/3318299.3318377,Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring,2019.0,International Conference on Machine Learning and Computing,Asif Ahmed Neloy; H. M. Sadman Haque; Md. Mahmud Ul Islam,"Apartment rental prices are influenced by various factors. The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors. An ensemble learning based prediction model is created to reach the goal. We have used a dataset from bProperty.com which includes the rental price and different features of apartments in the city of Dhaka, Bangladesh. The results show the accuracy and prediction of the rent of an apartment, also indicates the different types of categorical values that affect the machine learning models. Another purpose of the study is to find out the factors that signify the apartment rental price in Dhaka. To help our prediction we take on the Advance Regression Techniques (ART) and compare to different features of an apartment for establishing an acceptable model. The following algorithms are selected as the base predictors -- Advance Linear Regression, Neural Network, Random Forest, Support Vector Machine (SVM) and Decision Tree Regressor. The Ensemble learning is stacked of following algorithms -- Ensemble AdaBoosting Regressor, Ensemble Gradient Boosting Regressor, Ensemble XGBoost. Also, Ridge Regre",38.0, 79a347ddca012025f75d2565774cfe7adfb9a7ec,10.48044/jauf.1999.032,Valuation of Tree Aesthetics on Small Urban-Interface Properties,1999.0,Arboriculture & Urban Forestry,Richard P. Thompson; R. Hanna; Jay E. Noel; D. Piirto,"A model was developed to predict the value contribution of forest condition on small urban-wildland interface properties. Sample data were collected on property transactions in the Lake Tahoe Basin of California between 1990 and 1994. A variant of the stand density index (SDI) and a tree health measure were added to a list of traditional property characteristics (i.e., location, house size, lot size) to express the influence of tree care on property value. These aesthetic characteristics were statistically significant despite the expected dominant influence of the traditional characteristics. Values for the forest density and health characteristics were estimated and reveal a contribution to property value between 5% and 20%.",38.0,https://joa.isa-arbor.com/request.asp?JournalID=1&ArticleID=2859&Type=2 9bb59ce81ec3b84d9410e2ddbccdec4da9b85fc7,10.1002/agr.21581,The use of new‐media marketing in the green industry: Analysis of social media use and impact on sales,2018.0,Agribusiness,Becatien H. Yao; Aleksan Shanoyan; H. Peterson; C. Boyer; L. Baker,"During the recession, the decline in home value and home ownership reduced the demand for ornamental plants, lawn and garden products, and related services, which resulted in significantly negative effect on the green industry revenues. Postrecession consolidation in the United States green industry has forced smaller firms to re‐evaluate their marketing strategies. New‐media marketing has gained attention as a relatively low‐cost and high‐exposure marketing strategy. This paper utilizes binary logit and interval censored regression to examine the use and the impact of social‐media marketing in the green industry. The analysis was based on primary data from a survey of US nurseries and garden centers. The findings reveal that the daily use of social‐media marketing is largely driven by the network effect and the managers’ attitudes. They also indicate that the small‐sized firms receive higher returns from social media use in terms of increased sales compared with larger firms.",38.0, 8891481973de1ade675a35e628140568686d59da,10.1016/j.jue.2019.05.001,The rise and effects of homeowners associations,2019.0,Journal of Urban Economics,Wyatt Clarke; Matthew Freedman,"In the U.S., nearly 60% of recently built single-family houses, and 80% of houses in new subdivisions, are part of a homeowners association (HOA). We construct the first near-national map of HOAs using publicly recorded mortgage records for single-family homes. We use these data to document the growth and characteristics of HOAs as well as to examine their relationship with housing prices. We find that houses in HOAs have prices that are on average at least 4%, or $13,500, greater than observably similar houses outside of HOAs. The HOA premium correlates with the stringency of local land use regulation, local government spending on public goods, and measures of social attitudes toward race. The data also paint a detailed picture of the people living in HOA neighborhoods, who are on average more affluent and racially segregated than those living in other nearby neighborhoods.",38.0, 5c32a3c716b682cb3c2c807fde10e206b7d14f99,10.1016/j.spasta.2022.100723,"Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China",2022.0,Spatial Statistics,Binbin Lu; Yong Ge; Yilin Shi; Jianghuai Zheng; P. Harris,,38.0,https://repository.rothamsted.ac.uk/download/b6265a681f0826a2afe0bb804d8189772ddb669a32e5c2cadbed6272b5682092/2409353/Uncovering%20drivers.docx 65c899cce84e99c0088a6d325c72650cdc3034ff,10.1111/j.1468-2257.2006.00332.x,Externality Effects on Residential Property Values: The Example of Noise Disamenities,2006.0,,D. E. Clark,,38.0, eb4a6bf84eada4b6f16faea99b2c9b338120e625,10.1080/09654313.2012.673569,Spatial Determinants of Housing Price Values in Istanbul,2012.0,,T. K. Koramaz; V. Dokmeci,,38.0, e9a01934bb6af48f72ed97e6d6cebc3c4f8ebd62,10.1108/13664381211211046,Spatial variation as a determinant of house price: Incorporating a geographically weighted regression approach within the Belfast housing market,2012.0,,M. McCord; P. Davis; M. Haran; S. McGreal; David McIlhatton,,38.0, 4a626d3a0902cfcaac80b83abd4d9e123effa9bf,10.1186/s12889-016-3798-y,"Obesity, diet quality, physical activity, and the built environment: the need for behavioral pathways",2016.0,BMC Public Health,A. Drewnowski; A. Aggarwal; Wesley Tang; P. Hurvitz; Jason Y. Scully; O. Stewart; A. Moudon,"BackgroundThe built environment (BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet.MethodsThe Seattle Obesity Study (SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index (HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity (PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months’ exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors (HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally.ResultsNone of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict bett",37.0,https://bmcpublichealth.biomedcentral.com/track/pdf/10.1186/s12889-016-3798-y 8e5c41e376a590b828e048a05697c9c87982b767,10.1080/10511482.2020.1792528,Another Look at Location Affordability: Understanding the Detailed Effects of Income and Urban Form on Housing and Transportation Expenditures,2020.0,Housing Policy Debate,Carrie Makarewicz; Prentiss A. Dantzler; Arlie Adkins,"ABSTRACT Findings from a study using the Panel Survey of Income Dynamics (PSID) and detailed urban environment and transit data support the location affordability hypothesis. Households in location-efficient places spent significantly less on household transportation, enough to offset high housing costs. Walkable blocks and good transit especially contribute to these savings. But households with very low incomes (below 35% AMI) do not see significant enough savings. Authors recommend investments in transit, sidewalks, and economic development in disinvested areas; the preservation and creation of affordable housing of all types and tenures; and more supports for households with very low incomes. For decades, researchers have explored how location efficiency (LE) affects housing affordability, including incorporating transportation costs into a holistic housing affordability measure known as location affordability. Others have argued that estimated transportation savings from LE may be overstated because of limits in data and methods. Smart and Klein’s 2018 article in Housing Policy Debate analyzed the PSID and found “no evidence to support the location affordability hypothesis.” Co",37.0, 708c65958c402666049e1af9f18ee218ba2457b5,10.1155/2020/7621576,Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data,2020.0,,Xin-Min Liu; Lu Sun; Qiu-Xia Sun; Ge Gao,"Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provi",37.0,https://downloads.hindawi.com/journals/jat/2020/7621576.pdf 2dcb8131a40a94d76de65bb7569a8657c44cb842,10.1080/09644008.2020.1771696,How Much State and How Much Market? Comparing Social Housing in Berlin and Vienna,2020.0,German Politics,Susanne Marquardt; D. Glaser,"This article examines the effect of two different social housing systems on housing-market characteristics and affordability using a case study of Berlin and Vienna. Despite comparable framework conditions, both cities pursued very different social housing policies. While both cities are currently challenged by growing populations and rising house prices, we show that Vienna is more successful in providing affordable housing as it has created effective institutions to attract and retain private actors in this sector. In Berlin, in contrast, the current crisis reveals the lack of institutional capacity to actively pursue housing policy. We argue that different housing market outcomes can be attributed to fundamental differences in the organisation of social housing provision reflecting divergent state-market relations. While in Berlin social housing has been seen as a provisional measure to correct temporary market imbalances, Vienna’s continuous state participation in the housing market has created a framework of balanced rights and responsibilities for social and private actors to achieve social policy goals. Differences in funding arrangements and institutional embeddedness influ",37.0,https://www.tandfonline.com/doi/pdf/10.1080/09644008.2020.1771696?needAccess=true 80958fd2638f21cfbb1a72def7e62ae46db5ef0f,10.3368/le.96.3.366,Flood Risk Perception in the Housing Market and the Impact of a Major Flood Event,2020.0,Land Economics,Hannah Hennighausen; Jordan F. Suter,"The impact of flood events on flood risk perception has important implications for policy. Applying a novel dataset featuring the flooding extents from a severe event in Colorado, we disentangle inundated properties from “near misses,” defined as structures not directly flooded but located inside the 100-year floodplain. Using a triple-difference hedonic framework, we show that inundated properties inside the floodplain underwent a decrease in price after the flood, while near misses saw a relative price increase. We speculate that inundated properties are perceived as being riskier and near misses relatively less risky, suggesting the possible influence of the availability heuristic or Bayesian learning. (JEL Q54, Q58)",37.0,http://le.uwpress.org/content/96/3/366.full.pdf 9805e2b83bf03293e0de45e1021b33d64a76fedb,10.1080/10835547.2015.12091876,Measuring Highway Impacts on House Prices Using Spatial Regression,2015.0,,M. Allen; Grant W. Austin; M. Swaleheen,,37.0,https://www.tandfonline.com/doi/pdf/10.1080/10835547.2015.12091876?needAccess=true 83261931676ccb5dd148ae0780a13ea79e57abbe,10.1111/1540-6229.12170,On the Relation between Local Amenities and House Price Dynamics,2018.0,,Eli Beracha; Benjamin Gilbert; Tyler Kjorstad; Kiplan S. Womack,,37.0, 272d9f2108948561b227d9e7ed33686005ee829e,10.1080/00036846.2018.1441512,"The impact of income, economic openness and interest rates on housing prices in China: evidence from dynamic panel quantile regression",2018.0,,Hui-Ming Zhu; Zheng Li; Peng Guo,,37.0, db16c84a05b5836e39a3d937a5835a6bffe67fb0,10.1080/14697688.2016.1241424,Symmetric thermal optimal path and time-dependent lead-lag relationship: novel statistical tests and application to UK and US real-estate and monetary policies,2014.0,,Hao Meng; Hai-Chuan Xu; Wei-Xing Zhou; D. Sornette,"We present the symmetric thermal optimal path (TOPS) method to determine the time-dependent lead-lag relationship between two stochastic time series. This novel version of the previously introduced thermal optimal path (TOP) method alleviates some inconsistencies by imposing that the lead-lag relationship should be invariant with respect to a time reversal of the time series after a change of sign. This means that, if ‘X comes before Y’, this transforms into ‘Y comes before X’ under a time reversal. We show that a previously proposed bootstrap test lacks power and leads too often to a lack of rejection of the null that there is no lead-lag correlation when it is present. We introduce instead two novel tests. The first criterion, based on the free energy p-value , quantifies the probability that a given lead-lag structure could be obtained from random time series with similar characteristics except for the lead-lag information. The second self-consistent test embodies the idea that, for the lead-lag path to be significant, synchronizing the two time series using the time varying lead-lag path should lead to a statistically significant correlation. We perform intensive synthetic test",36.0,https://arxiv.org/pdf/1408.5618 7b3ce56694f3995eac863eb9e312c76ddbe6882a,10.1016/j.watres.2019.115134,Decentralized water collection systems for households and communities: Household preferences in Atlanta and Boston.,2019.0,Water Research,Zhongming Lu; W. Mo; B. Dilkina; K. Gardner; S. Stang; Ju-Chin Huang; Maria Christina Foreman,"Development of sustainable and resilient water infrastructure is an urgent challenge for urban areas to secure long-term water availability and mitigate negative impacts of water consumption and urban development. A hybrid system that combines centralized water infrastructure and household decentralized water facilities, including rainwater harvesting and greywater recycling, may be a solution to more sustainable and resilient water management in urban areas. Understanding household and community preferences for decentralized water facilities is important to inform the design and ultimately the promotion and adoption of such systems. In this study, we conducted a discrete choice experiment, via Amazon Mechanical Turk, to collect data on household choices of different decentralized water facility designs in two U.S. cities, Atlanta, Georgia and Boston, Massachusetts. Based on the responses to the choice experiment, we then developed a latent-class choice model to predict households' preferences of decentralized system design features and examine the influence of socioeconomic and personal characteristics on heterogeneous class membership. We identified six major classes of preferenc",36.0,https://doi.org/10.1016/j.watres.2019.115134 9e052d47ef3655aa6be9947e4669d77bb349cea9,10.1016/j.jue.2018.07.001,"Land-use regulations, property values, and rents: Decomposing the effects of the California Coastal Act",2018.0,Journal of Urban Economics,Christopher Severen; A. Plantinga,"Land-use regulations can lower real estate prices by imposing costs on property owners, but may raise prices by restricting supply and generating amenities. We study the effects of the California Coastal Act (CCA), one of the nation’s most stringent land-use regulations, on prices and rents for multifamily housing units. The CCA applies to a narrow section of the California coast, allowing us to compare properties on either side of the jurisdictional boundary. The CCA offers several advantages for measuring the effects of land-use regulations, including plausible exogeneity of the boundary location, which we confirm using historical data on boundary placement, and orthogonality of the boundary to other jurisdictional divisions. Extending previous studies, we decompose the effects of the regulation into a local effect, the net price effect of restrictions on the subject property and its immediate neighbors, and an external effect, the value of amenities generated by restrictions on all properties within the regulated area. Data on multifamily housing rents are used to isolate the effect of restrictions on adjacent properties, which we term the neighbor effect. Our analysis of multif",36.0,https://doi.org/10.2139/ssrn.2810297 6dab7efd0c9de11307864267024c7235d8fa6813,10.5755/j01.ee.28.2.13777,Influential Factors on the Market Value of Residential Properties,2017.0,,Nadja Ferlan; M. Bastic; I. Pšunder,"When using sales comparison approach, it is crucial to apply adjustments based on the differences between the appraised and comparable properties. Estimating the significance of each adjustment is of key importance for performing credible valuation. Adjustments should include factors such as location, physical and functional characteristics of residential properties and external influential factors. The theoretical part of the article presents the results of research on the importance and impact of various influential factors on the value of residential properties, which have been carried out in various parts of the world. The empirical part of the article presents analytical results about the importance of factors influencing the estimated value of residential properties as presented by different experts. The following factors were included in the study: orientation, views from the property, noise, number of storeys and the age of the building. For the purpose of valuation practice, the linear function and annual depreciation rate have been set. Data needed in the study were collected through interviews with authorised real estate appraisers, court sworn appraisers and real proper",36.0,https://zenodo.org/record/1121671 bdc6627e4692efb14740c7251a406943ebff8036,10.2139/ssrn.1787252,Credit supply and house prices: Evidence from mortgage market segmentation,2014.0,Journal of Financial Economics,M. Adelino; A. Schoar; Felipe Severino,"We show that easier access to credit significantly increases house prices by using exogenous changes in the conforming loan limit as an instrument for lower cost of financing. Houses that become eligible for financing with a conforming loan show an increase in house value of 1.16 dollars per square foot (for an average price per square foot of 220 dollars) and higher overall house prices controlling for a rich set of house characteristics. However, these estimated coefficients are consistent with a local elasticity of house prices to interest rates that is lower than some previous studies proposed (below 10). In addition, loan to value ratios around the conforming loan limit deviate significantly from the common 80 percent norm, which confirms that it is an important factor in the financing choices of home buyers. In line with our interpretation, the results are stronger in the first half of our sample (1998-2001) when the conforming loan limit was more important, given that other forms of financing were less common and substantially more expensive. Results are also stronger in zip codes where personal income growth is low or declining, and in regions with lower elasticity of housi",36.0,https://doi.org/10.2139/ssrn.1787252 f165026a515e43c197e3e4d1c87746bf5ede29d2,10.1016/j.apr.2019.06.001,Exploring the spatial effect of urbanization on multi-sectoral CO2 emissions in China,2019.0,Atmospheric pollution research,Ya-Nan Wang; Xin Luo; Wei Chen; Min-Juan Zhao; Bowen Wang,"Abstract Rapid urbanization has increased the demand for energy in various sectors. Previous studies have focused more on individual sectors when investigating the spatial impact of urbanization on CO2, but few have compared the differences among sectors. This study uses geographically weighted regression model to investigate the effect of urbanization on CO2 emissions in six sectors in 2000, 2005, 2010, and 2015. Industrial structure and energy intensity were selected as control variables in this study. The results indicate that the impact of urbanization on CO2 emissions in agriculture, forestry, animal husbandry, fishery and water conservancy sector declines from the southeastern coastal area to the northwest. The influence of urbanization on CO2 emissions presents an increasing trend from the northeast region to the southwest region in the construction industry sector. In industry and residential consumption sectors, urbanization had a greater impact on CO2 emissions in the eastern region than in the western region. However, urbanization had a stronger impact on CO2 emissions in the western region than in the eastern region in the transportation, storage, and postal industry se",36.0, 6bf2617c5dce583da4d3e77c7f66b17ac6cf1a53,10.1016/j.cities.2018.01.018,"The determinants of informal housing price in Beijing: Village power, informal institutions, and property security",2018.0,Cities,Meng-Zhu Zhang; Peng-Jun Zhao,"Abstract Although the how's of the informal housing market in China has been discussed widely; the answers remain conflicted. Using the theory of new institutional economics, this paper aims to answer two questions: what informal institutions secure the de facto tenure and facilitate the market? and How do informal institutions affect the pricing mechanism? We propose a conceptual framework to investigate the links between informal institutions, de facto tenure, and the price of informal housing. It is argued that villages have the ability to seek political patronage and regulate the market transactions to secure the tenure of informal housing. This ability is authorized by several formalities: the dual land system and the regime of village autonomy. The growing powers of market agencies in local development help strengthen the village's ability. The variances in ability between villages lead to a heterogeneity of tenure security, thus creating price differentials in the market. A higher tenure security increases the housing price. In conclusion, the informal housing market in China is “regulated” by a “semi-formal” system in which both informality and formality work and intertwine",36.0, 91515d891035128ee553dcfe9a069aa83d31f8e5,10.1080/08111146.2022.2124966,We Zoned for Density and Got Higher House Prices: Supply and Price Effects of Upzoning over 20 Years,2020.0,Urban Policy and Research,Cameron K. Murray; M. Limb,"ABSTRACT Does planning for higher density increase housing development and decrease prices? We study nineteen repeatedly upzoned densification areas in Brisbane, Australia. We create a site-level dataset on dwelling stock, planning regulations, and prices, comprising all 25,775 sites in these areas. Only 2% of the zoned capacity was used in any five-year period. Zoned capacity doubled over the twenty-year study period, but 78% of sites with zoned capacity in the first period remained undeveloped. Higher rates of new housing supply are robustly related to higher prices. Zoned capacity has no relationship to price across numerous regression models.",36.0,https://eprints.qut.edu.au/235371/1/115770485.pdf d79aff3af45de5fe5fb16fc423114a546ba2bfd5,10.1016/j.econmod.2021.03.015,A spatial difference-in-differences approach to evaluate the impact of light rail transit on property values,2021.0,Economic Modelling,F. Qiu; Q. Tong,"Abstract How to evaluate the comprehensive costs and benefits of urban light trains is a critical issue. The difference-in-differences technique is widely used in the literature. However, the conventional approach assumes the project does not affect the control group, which might be violated in real life due to social and economic interactions. We refine the DID approach and relax the no-spillover assumption. Through an example of impacts of a new light rail transit on house prices in Edmonton, Canada, we illustrate how the approach can be used to calculate three different treatment effects. The results indicate that the train system has negative impacts on values of single-detached houses nearby the train stations, and the negative effects also spill over to properties outside the treatment zone. Our findings show that the direction and magnitude of project impacts depend on the city size, the design of the train system, and relevant neighborhood characteristics.",36.0, c9e1ea7fa46747a0d33a78c660fe5e7a2b44bbc5,10.1111/jors.12447,House prices and school choice: Evidence from Chicago's magnet schools’ proximity lottery,2019.0,Journal of Regional Science,Leonardo Bonilla-Mejía; Esteban López; Daniel P. McMillen,"Studies of open school policies predict house prices to rise in areas that gain access to high‐quality schools. However, excess demand may limit access to high‐quality schools. We take advantage of changes in Chicago's schools’ admissions policies to test whether a higher probability of admission to magnet schools for students living within 1.5 miles leads to higher house prices. Results indicate that the 1997 and 2009 reforms increased house prices for homes within the 1.5 mile radius by about 4% and 12.6%, respectively. The higher probability of admission for black students after a consent decree was vacated in 2009 led to a significant increase in prices in predominantly African‐American areas on the south side.",36.0, e60a96b0d3e8982f599edddb5fe7aa693c2b938d,10.5038/2375-0901.15.4.3,The Effect of Proximity to Urban Rail on Housing Prices in Ottawa,2012.0,,C. Hewitt; W. Hewitt,,36.0,https://digitalcommons.usf.edu/cgi/viewcontent.cgi?article=1080&context=jpt a18278a033e439950b2f69e1ce4266867e3394a2,10.2139/ssrn.771867,"Spatial Dependence, Housing Submarkets, and House Prices",2005.0,,Steven C. Bourassa; E. Cantoni; M. Hoesli,,36.0,https://doi.org/10.2139/ssrn.771867 915683aac8519f9307fa14065e1c8149e5381fa8,10.1068/a050357,Measuring the Determinants of Relative House Prices,1973.0,,R. Wilkinson; C. Archer,,36.0, f4e7ad57e8cecf6df00b9f645c303006086203bd,10.1016/j.jhe.2016.06.002,The effect of mortgage interest deduction and mortgage characteristics on house prices,2016.0,,S. Damen; F. Vastmans; E. Buyst,,36.0, c9fd961913713132e63960b88be0bc5a3e15f99f,10.3390/app122412942,GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest,2022.0,Applied Sciences,Yunfan Gao; Yun Xiong; Siqi Wang; Hao-Fen Wang,"Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activities, widely used to extract geospatial information. However, previous studies often focus on a specific area, such as a city or a district, and are designed only for particular tasks, such as land-use classification. On the other hand, large-scale pre-trained models (PTMs) have recently achieved impressive success and become a milestone in artificial intelligence (AI). Against this background, this study proposes the first large-scale pre-training geospatial representation learning model called GeoBERT. First, we collect about 17 million POIs in 30 cities across China to construct pre-training corpora, with 313 POI types as the tokens and the level-7 Geohash grids as the basic units. Second, we pre-train GeoEBRT to learn grid embedding in self-supervised learning by masking the POI type and then predicting. Third, under the paradigm of “pre-training + fine-tuning”, we design five practical downstream tasks. Experiments show that, ",35.0,https://www.mdpi.com/2076-3417/12/24/12942/pdf?version=1671184458 f384fb58c99f36cbcc727d28bbd9a3f623cdb1de,10.3389/fenvs.2022.1009094,TOD and vibrancy: The spatio-temporal impacts of the built environment on vibrancy,2022.0,Frontiers in Environmental Science,Bingjie Yu; Xu Cui; Hong Li; Pin-Yang Luo; Run-Ze Liu; Tian Yang,"Urban vibrancy is described by the activities of residents and their spatio-temporal dynamics. The metro station area (MSA) is one of the densest and most populous areas of the city. Thus, creating a vibrant and diverse urban environment becomes an important goal of transit-oriented development (TOD). Existing studies indicate that the built environment decisively determines MSA-level urban vibrancy. Meanwhile, the spatio-temporal heterogeneity of such effects requires thoroughly exploration and justification. In this study, we first apply mobile signaling data to quantify and decipher the spatio-temporal distribution characteristics of the MSA-level urban vibrancy in Chengdu, China. Then, we measure the built environment of the MSA by using multi-source big data. Finally, we employ geographically and temporally weighted regression (GTWR) models to examine the spatio-temporal non-stationarity of the impact of the MSA-level built environment on urban vibrancy. The results show that: 1) The high-vibrant MSAs concentrate in the commercial center and the employment center. 2) Indicators such as residential density, overpasses, road density, road network integration index, enterprise de",35.0,https://www.frontiersin.org/articles/10.3389/fenvs.2022.1009094/pdf fa42a1ba56bbe4b066f0cf9fba224e67c6e1c2af,10.3390/su13041771,Evaluation and Optimization of Urban Land-Use Efficiency: A Case Study in Sichuan Province of China,2021.0,Sustainability,Mengchao Yao; Yi-Hua Zhang,"In the recent 10 years, China’s housing prices and land prices have risen rapidly, resulting in precious land resources, thus restricting the development of the cities. How to effectively measure urban land-use efficiency and how to optimize it has become a stumbling block on the road of sustainable development in China. This article focuses on the vital province in southwest China—Sichuan Province, which is facing the problem of insufficient land-use efficiency and uses the data of 32 cities from 2003 to 2018 to carry out the research. Based on the measurement results of urban land-use efficiency in Sichuan Province and its temporal and spatial evolution characteristics, this paper uses the SDM model to verify that the land-use efficiency can be optimized from the three aspects of innovation, industrial structure, and economic connections. The conclusion shows: (1) There is a weak decoupling relationship between urban land use and economic development in Sichuan Province. The urban land-use efficiency has the characteristics of polarization of more than two ends and less in the middle, but the gap is gradually reduced; (2) Time series level, urban land-use efficiency in various re",35.0,https://www.mdpi.com/2071-1050/13/4/1771/pdf?version=1612756643 39e0036c264681a3bff9f494704e4e8b48631556,10.5888/pcd11.140135,"The Geography of Diabetes by Census Tract in a Large Sample of Insured Adults in King County, Washington, 2005–2006",2014.0,Preventing Chronic Disease,A. Drewnowski; C. Rehm; A. Moudon; David E. Arterburn,"Introduction Identifying areas of high diabetes prevalence can have an impact on public health prevention and intervention programs. Local health practitioners and public health agencies lack small-area data on obesity and diabetes. Methods Clinical data from the Group Health Cooperative health care system were used to estimate diabetes prevalence among 59,767 adults by census tract. Area-based measures of socioeconomic status and the Modified Retail Food Environment Index were obtained at the census-tract level in King County, Washington. Spatial analyses and regression models were used to assess the relationship between census tract–level diabetes and area-based socioeconomic status and food environment variables. The mediating effect of obesity on the geographic distribution of diabetes was also examined. Results In this population of insured adults, diabetes was concentrated in south and southeast King County, with smoothed diabetes prevalence ranging from 6.9% to 21.2%. In spatial regression models, home value and college education were more strongly associated with diabetes than was household income. For each 50% increase in median home value, diabetes prevalence was 1.2 perc",35.0,https://www.cdc.gov/pcd/issues/2014/pdf/14_0135.pdf 4195fc4a67164f497e810de0b585e0b4a3496776,10.2139/ssrn.3364630,Trends in Household Debt and Credit,2019.0,Social Science Research Network,A. Haughwout; Donghoon Lee; Joelle W. Scally; Lauren Thomas; H. Wilbert van der Klaauw,"Since the onset of the 2008 financial crisis, consumer financial and borrowing behavior, once considered a relatively quiet little corner of finance, has been of enormously increased interest to policymakers and researchers alike. Prior to the Great Recession, there was a historic run-up in household debt, driven primarily by housing debt, which coincided with a speculative bubble and sharp rises in home prices. Then, as prices began to fall, millions of households began defaulting on their mortgages, unable to keep up with home payments, and greatly contributing to the onset of the deepest recession since the 1930s. Following the steep increase in debt balances during the boom, households began rapidly paying off their loans during and immediately after the Great Recession. Since 2013, debt has begun to increase and eventually rise above its previous levels, albeit at a much slower rate than before, at least partially owing to stricter lending standards. We examine the trends in household debt before, during, and since the 2000s financial crisis and Great Recession. As we will show, this period is unique in American history in several ways. Our analysis will show the sources of th",35.0,https://doi.org/10.2139/ssrn.3364630 0dfdeb42df881033b411b5223f7ee8753a4cada0,10.1016/j.retrec.2020.100900,Economic impacts of being close to subway networks: A case study of Korean metropolitan areas,2020.0,,Kwangwon Ahn; H. Jang; Yena Song,"Abstract Subway networks are often developed in densely populated metropolitan areas that have enough potential passengers to merit their operation. Once a network has been constructed, it provides mobility to passengers and improves local accessibility to various destinations, thereby having economic, social, and public health impacts. This study aims to examine the economic impacts of subway networks in metropolitan areas among the various, wide-ranging effects of provision of transit networks. The association between actual transaction prices of condominiums and accessibility to subway networks in four metropolitan areas in South Korea is examined. Condominiums make up the dominant housing type in South Korea, so they provide a valid proxy for housing prices. In the modeling process, factors known to have a close relationship with housing prices are integrated along with the accessibility of subway networks, and spatially lagged models are utilized to effectively deal with the spatial patterns. The results of this research show that the expected positive effects of transit accessibility exist in the majority of cases in metropolitan areas, though one metropolis was an exception.",35.0,https://hdl.handle.net/2123/27145 bd6f91753c8d8d26bca64b3eebfbdeb23471a163,10.3386/w25174,Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks,2018.0,,E. Glaeser; Michael S. Kincaid; N. Naik,"How much does the appearance of a house, or its neighbors, impact its price? Do events that impact the incentives facing homeowners, like foreclosure, impact the maintenance and appearance of a home? Using computer vision techniques, we find that a one standard deviation improvement in the appearance of a home in Boston is associated with a .16 log point increase in the home’s value, or about $68,000 at the sample mean. The additional predictive power created by images is small relative to location and basic home variables, but external images do outperform variables collected by in-person home assessors. A home’s value increases by .4 log points, when its neighbor’s visually predicted value increases by one log point, and more visible neighbors have a larger price impact than less visible neighbors. Homes that went through foreclosure during the 2008-09 financial crisis experienced a .04 log point decline in their appearance-related value, relative to comparable homes, suggesting that foreclosures reduced the incentives to maintain the housing stock. We do not find more depreciation of appearance in rental properties, or more upgrading of appearance by owners before resale.",35.0,https://doi.org/10.3386/w25174 6c30a068914ec9a68ff89a0cea83098ee8481aa8,10.24149/gwp401r1,Land Price Dynamics and Macroeconomic Fluctuations with Imperfect Substitution in Real Estate Markets,2021.0,Social Science Research Network,J. S. Davis; Kevin X. D. Huang; Ayşe Sapci,"The collateral channel, whereby an increase in residential house prices leads to an increase in commercial property prices, loosening firm borrowing constraints and leading to higher firm investment, is weaker when residential and commercial real estate are imperfect substitutes. We first show in a reduced form regression with firm level data that the strength of local zoning regulations has a negative effect on the estimated increase in firm investment following an increase in local residential real estate prices. We then modify the DSGE model of the collateral channel in Liu, Wang, and Zha (2013) to allow imperfect substitutability between residential and commercial land. With Bayesian estimation and U.S. data, we estimate that the elasticity of substitution between the two types of land is 0.88. Variance decompositions and impulse responses show that the strength of the collateral channel linking house prices and investment is weaker when the two types of land are imperfect substitutes.",35.0,https://doi.org/10.24149/gwp401r1 640fffe1e1529dd6105ffc662a552d7cc71a8ae2,10.5539/jsd.v4n2p48,"Impact of Sanitary Landfills on Urban Residential Property Value in Lagos State, Nigeria",2011.0,,O. A. Akinjare; C. Ayedun; A. Oluwatobi; O. Iroham,"Landfills have for long been generally thought to have diminutionary effect on residential property values. Various researches sparked off by this contention have proved contradictory in some instances. This study attempts to examine the impact of the four State established landfills on residential property values in Lagos State, each of varing sizes and having various degrees of contamination. A total of  229, 2 341 and 315 structured questionaires were administerd to Estate Surveyors and Valuers, residents within 1.2km distance from four landfills and Lagos State Waste Management Agency (LAWMA) workers respectively and on the avearge, 78% were retrieved.  Evaluation using linear regression function of data drawn from administered questionaires showed a slight evident of statistical significance indicating that all residential property values increased with distances away from landfill sites at an average of 6% for the four landfills.",35.0,https://ccsenet.org/journal/index.php/jsd/article/download/10172/7272 c39f401997403328752b9239dcb5d662c3333799,10.1080/23311975.2020.1831765,Determinants of household debt in emerging economies: A macro panel analysis,2020.0,,Khairunnisa Abd Samad; S. N. Mohd Daud; Nuradli Ridzwan Shah Mohd Dali,"Abstract A rapid increase in household debt is undeniably a main concern among policymakers. Studies indicating the damaging effect of rapid rise in household debt towards economic growth attracted many researchers to determine its reasons. The risk from high household debt is not only applicable to advanced economies, but also inherent in emerging economies. Thus, the present study examines the leading causes of household debt in emerging economies. The study employs a bias-corrected least square dummy variable for the period of 1995–2018. The results show positive and significant effects of financial development, house prices, and lending interest rate. Meanwhile, unemployment rate and inflation are negatively associated with household debt. The study therefore urges policymakers, relevant authority and financial institutions to employ suitable and effective policy to mitigate the factors identified in the rise of household debt.",35.0,https://www.tandfonline.com/doi/pdf/10.1080/23311975.2020.1831765?needAccess=true bfecec0495ab22fb487076c8bd2e359a31ac6024,10.1177/0160017617749283,International Migration and Regional Housing Markets: Evidence from France,2017.0,Social Science Research Network,Hippolyte d'Albis; Ekrame Boubtane; D. Coulibaly,"This article examines the causal relations between immigration and the characteristics of the housing market in host regions. We constructed a unique database from administrative records and used it to assess annual migration flows into France’s twenty-two administrative regions from 1990 to 2013. We then estimated various panel vector autoregression models, taking into account gross domestic product per capita and the unemployment rate as the main regional economic indicators. We find that immigration has no significant effect on property prices but that higher property prices significantly reduce immigration rates. We also find no significant relationship between immigration and social housing supply.",35.0,https://hal.science/hal-01469758 32a0805b769229ccee166d6c0009ef7c08ddad98,10.1109/nana56854.2022.00082,An optimized prediction algorithm based on XGBoost,2022.0,International Conference on Networking and Network Applications,Cheng Sheng; Hai-Zheng Yu,"The real estate market is closely related to people's life. It is very important to accurately predict the future real estate price. Traditional methods are difficult to describe the nonlinear characteristics of house price prediction. XGBoost algorithm can effectively represent the nonlinear relationship in house price prediction. However, the selection of parameters determines the learning and generalization ability of XGBoost, and it is very important to determine the parameters of XGBoost. Particle swarm optimization algorithm can select the training parameters of XGBoost more quickly and accurately. Therefore, this paper studies the house price prediction based on the hybrid model of particle swarm optimization XGBoost algorithm, namely PSO-XGBoost model. Using the collected sample data of houses in Ames, Iowa, five different machine learning algorithms including PSO-XGBoost are used to predict house prices. Finally, the results of five algorithms are compared and analyzed. The experimental results show that PSO-XGBoost model has the highest prediction accuracy and the best effect, and the prediction effect of integrated learning algorithm is better than that of linear regress",35.0, 9d356772ecd7824d982ab3bab1b48bd29ff19583,10.1017/s1074070800022859,"Does Close Count? School Proximity, School Quality, and Residential Property Values",2007.0,Journal of Agricultural and Applied Economics,K. Owusu-Edusei; M. Espey; Huiyan Lin,,35.0,https://ageconsearch.umn.edu/record/6609/files/39010211.pdf ce39b5e0456948444e3f8dcbbe9a114c08abd7d6,10.3390/app10217722,"Evaluation of the Built-Up Area Dynamics in the First Ring of Cluj-Napoca Metropolitan Area, Romania by Semi-Automatic GIS Analysis of Landsat Satellite Images",2020.0,Applied Sciences,B. Dolean; Ștefan Bilașco; D. Petrea; C. Moldovan; I. Vescan; S. Roșca; I. Fodorean,"The accentuated dynamics of the real estate markets of the last 20 years, determined that a large part of the territories in the immediate vicinity of the big urban centers, to change their category of land use, in an accelerated rhythm. Most of the time, the land use changes according to the market requirements, the predominantly agricultural lands being occupied by constructions with residential or industrial functions. Identifying these changes is a difficult task due to the heterogeneity of spatial databases that come from different real estate development projects, so determining and implementing new methods to track land changes are currently highly required. This paper presents a methodologically innovative index-based approach for the rapid mapping of built-up areas, using Landsat-5, Landsat-7, and Landsat-8 satellite imagery. The approach described in this study differs from other conventional methods by the way the analysis was performed and also by the thematic indices used in the processes of built-up area delineation. The method, structured in a complex model, based on Remote Sensing and GIS techniques, can be divided into three distinct phases. The first stage is rela",34.0,https://www.mdpi.com/2076-3417/10/21/7722/pdf?version=1604301718 9e18fc41122dab893971e884efb3d81ada06b9e4,10.3390/land10090984,Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example,2021.0,Land,Yunzi Yang; Yuanyuan Ma; Hongzan Jiao,"Block is the basic unit for studying the urban activities of residents, and block vitality is the concrete expression of urban dynamics at the block level. The quality of the block’s residential environment is a crucial medium to satisfy the residents’ pursuit of high-quality life; good block quality is essential for fostering the block vitality and further enhancing the overall vitality of the city. This study used the distribution density of cellular signaling data to quantify block vitality and constructed a block environment index system covering four dimensions—block accessibility, block function, block development degree, and human perception of block environment—innovatively introducing the elements of block environment from the human perspective. Considering the variability of block vitality between workdays and weekends, and between downtown and suburban blocks, this study used a geographically weighted regression model to show the mechanism of the spatial and temporal influence of indicators on block vitality, as well as to suggest how to enhance block vitality at different times of the day based on the influence mechanism. This study was conducted in Wuhan, China. The fi",34.0,https://doi.org/10.3390/land10090984 d66753be2a0cf42d87d9ce4ba12179f9965de3f0,10.1016/j.amepre.2014.05.006,Environments Perceived as Obesogenic Have Lower Residential Property Values,2014.0,American Journal of Preventive Medicine,A. Drewnowski; A. Aggarwal; C. Rehm; Hannah Cohen-Cline; P. Hurvitz; A. Moudon,"Background Studies have tried to link multiple aspects of the built environment with physical activity and obesity rates. Purpose To determine the relation between residential property values and multiple perceived (self-reported) measures of the obesogenic environment. Methods The Seattle Obesity Study (SOS) used a telephone survey of a representative, geographically distributed sample of 2,001 King County adults, collected in 2008–2009 and analyzed in 2012–2013. Home addresses were geocoded. Residential property values at the tax parcel level were obtained from the King County tax assessor. Mean residential property values within a 10-minute walk (833-meter buffer) were calculated for each respondent. Data on multiple perceived measures of the obesogenic environment were collected by self-report. Correlations and multivariable linear regression analyses, stratified by residential density, were used to examine the associations among perceived environmental measures, property values, and BMI. Results Perceived measures of the environment such as crime, heavy traffic, and proximity to bars, liquor stores, and fast food were all associated with lower property values. By contrast, liv",34.0,https://europepmc.org/articles/pmc4142077?pdf=render 61593569a5cafd797ea0925a6f82c1eb8d5500ee,10.5198/jtlu.2021.1805,Understanding jobs-housing imbalance in urban China: A case study of Shanghai,2021.0,,Weiye Xiao; Dennis Wei; Han-Duo Li,"Shanghai has experienced a rapid process of urbanization and urban expansion, which increases travel costs and limits job accessibility for the economically disadvantaged population. This paper investigates the jobs-housing imbalance problem in Shanghai at the subdistrict-level (census-level) and reaches the following conclusions. First, the jobshousing imbalance shows a ring pattern and is evident mainly in the suburban areas and periphery of the Shanghai metropolitan area because job opportunities are highly concentrated while residential areas are sprawling. Second, structural factors such as high housing prices and sprawling development significantly contribute to the jobs-housing imbalance. Third, regional planning policies such as development zones contribute to jobs-housing imbalance due to the specialized industrial structure and limited availability of housing. However, geographically weighted regression reveals the development zones in the traditional Pudong district are exceptional insofar as government policy has created spatial heterogeneity there. In addition, the multilevel model used in this study suggests regions with jobs-housing imbalance usually have well-connec",34.0,https://www.jtlu.org/index.php/jtlu/article/download/1805/1551 7fb4ba5e971905e2d899efe5fd0e0764fe6ed10c,10.1007/s13209-015-0136-z,Measuring expectations from household surveys: new results on subjective probabilities of future house prices,2015.0,SERIEs: Journal of the Spanish Economic Association,O. Bover,"I analyze new data on subjective probabilistic expectations on house prices collected in the Spanish Survey of Household Finances. Households are asked to distribute ten points among five different scenarios for the change in the price of their homes over the next 12 months. This paper is the first empirical study to document the beliefs of a representative sample of households about the future value of their homes. It also reviews the methodology of expectation measurement and recent work on household subjective probabilities. I model individual subjective probability densities using splines, construct quantiles from those densities, and analyze how the heterogeneity in the individual distributions relates to differences in housing and household characteristics. An important result of the paper is that women are more optimistic about the evolution of house prices than men. Location at the postal code level accounts for a large fraction of the variation in the subjective distributions across households. Finally, I provide some results on how subjective expectations matter for predicting spending behavior. Housing investment and car purchases are negatively associated with pessimist",34.0,https://doi.org/10.2139/ssrn.2696006 b8a801f6e2315e48457b64db62faee01bf52caa7,10.1088/1757-899x/263/4/042098,Real estate value prediction using multivariate regression models,2017.0,,R. Manjula; Shubham Jain; S. Srivastava; Pranav Rajiv Kher,"The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.",34.0,https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042098/pdf d853177c069989c00fc0c09f38534dfb4cc01462,10.1111/ecoj.12489,"Income Taxes, Sorting, and the Costs of Housing: Evidence from Municipal Boundaries in Switzerland",2017.0,Social Science Research Network,C. Basten; M. von Ehrlich; A. Lassmann,"This paper provides novel evidence on the role of income taxes for residential rents and spatial sorting. Drawing on comprehensive apartment-level data, we identify the effects of tax differentials across municipal boundaries in Switzerland. The boundary discontinuity design (BDD) corrects for unobservable location characteristics such as environmental amenities or the access to public goods and thereby reduces the estimated response of housing prices by one half compared to conventional estimates: we identify an income tax elasticity of rents of about 0.26. We complement this approach with census data on local sociodemographic characteristics and show that about one third of this effect can be traced back to a sorting of high-income households into low-tax municipalities. These findings are robust to a matching approach (MBDD) which compares identical residences on opposite sides of the boundary and a number of further sensitivity checks.",34.0,https://boris.unibe.ch/143545/1/CRED%20Research%20Paper%20No.%205.pdf b8ec0244e6009f8e7cdc732fe51235469809b034,10.1080/01621459.2019.1686392,Testing the Predictability of U.S. Housing Price Index Returns Based on an IVX-AR Model,2020.0,Journal of the American Statistical Association,Bing-Duo Yang; Wei Long; Liang Peng; Zong-Wu Cai,"Abstract We use ten common macroeconomic variables to test for the predictability of the quarterly growth rate of house price index (HPI) in the United States during 1975:Q1–2018:Q2. We extend the instrumental variable based Wald statistic (IVX-KMS) proposed by Kostakis, Magdalinos, and Stamatogiannis to a new instrumental variable based Wald statistic (IVX-AR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model. Simulation results show that the proposed IVX-AR exhibits excellent size control regardless of the degree of serial correlation in the error terms and the persistency in the predictive variables, while IVX-KMS displays severe size distortions. The empirical results indicate that the percentage of residential fixed investment in GDP is fairly a robust predictor of the growth rate of HPI. However, other macroeconomic variables’ strong predictive ability detected by IVX-KMS is likely to be driven by the highly correlated error terms in the predictive regressions and thus becomes insignificant when the proposed IVX-AR method is implemented. Supplementary materials for this article, including a standardized d",34.0, 675f5c74687318016dea93671393315268d40e34,10.1016/j.forpol.2021.102420,Urban green spaces and housing prices in developing countries: A Two-stage quantile spatial regression analysis,2021.0,,Manuel A. Zambrano-Monserrate; M. A. Ruano; Cristina Yoong-Parraga; C. Silva,"Abstract Urban Green Spaces (UGS) provide various environmental benefits to nature and are essential for people's overall well-being. UGS regulate temperature and humidity, absorb pollutants, and reduce soil erosion. Also, they improve the emotional and physical well-being of people and increase the environmental awareness of citizens. Due to its great importance, various studies have sought to economically quantify its value through the housing market. However, the households' heterogeneity and the vegetation index of the UGS have not been analyzed together. In this study, we show that a developing country's population values the UGS (economically). Still, that valuation depends on the housing submarket and the interaction between “distance” and vegetation index. For example, for small parks, the interaction between “distance” and vegetation index is significant only in the highest and lowest conditional quantiles. For medium and large parks, the interaction is significant only from the 50th and 30th percentiles, respectively. On the other hand, for small, medium, and large forests, the interactive variable is significant starting from the 50th, 70th, and 80th percentiles, respect",34.0, 48bc9edb49b1960fce98edac012b2f0e5496305c,10.5089/9781451873788.001,"Three Cycles: Housing, Credit, and Real Activity",2009.0,Social Science Research Network,Natalia T. Tamirisa; A. Kabundi; Deniz Igan; Francisco Nadal De Simone; M. Pinheiro,"We examine the characteristics and comovement of cycles in house prices, credit, real activity and interest rates in advanced economies during the past 25 years, using a dynamic generalised factor model. House price cycles generally lead credit and business cycles over the long term, while in the short to medium run the relationship varies across countries. Interest rates tend to lag other cycles at all time horizons. While global factors are important, the U.S. business cycle, house price cycle and interest rate cycle tend to lead the respective cycles in other countries over all time horizons. However, the U.S. credit cycle leads mostly over the long term.",34.0,https://www.elibrary.imf.org/downloadpdf/journals/001/2009/231/001.2009.issue-231-en.xml ff2384e4914f2b300a7fb0d9314ce184a151933e,10.1145/2996913.2996960,"A spatio-temporal, Gaussian process regression, real-estate price predictor",2016.0,SIGSPATIAL/GIS,H. Crosby; Paul Davis; T. Damoulas; S. Jarvis,"This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.",34.0,https://doi.org/10.82444/warw.33729535 4ab45320db87c34f68dbcc686aab22e949008a89,10.1016/j.regsciurbeco.2017.11.001,Spatial dependence in apartment transaction prices during boom and bust,2018.0,,Dongwoo Hyun; Stanimira Milcheva,,34.0,https://discovery.ucl.ac.uk/10046318/1/RegScienceAndUrbEcon%20-%20Huyn_Milcheva_Spatial%20Dependence_ISIS.pdf cde8f5b9c66983eafd39cde099f4b5d18ba9eee3,10.1108/ijhma-01-2018-0001,"Housing prices in peninsular Malaysia: supported by income, foreign inflow or speculation?",2019.0,International Journal of Housing Markets and Analysis,Wang Wong; Chin Lee; S. Koong," Purpose This paper is motivated by a concern about the ability of the average Malaysian income to catch up with the rapidly increasing house prices in Peninsular Malaysia. Financial innovation in financial system now regards houses as a financial asset and speculation vehicle. Therefore, a house purchase is made to acquire not merely a necessity but also a financial asset which can generate future returns. Given the problems in the housing market, this paper aims to examine the determinants of house prices in Malaysia, including those such as income, population, foreign inflow and speculation. Design/methodology/approach This study adopts panel data analyses, namely, the fixed effect model (FEM) and the pooled mean group (PMG), and uses data at state level in quarterly frequency, spanning from 2005Q1 to 2013Q4. Findings Based on the results of FEM, these determinants influence house prices significantly. Moreover, the PMG results suggest that there is convergence in the model, which are indicated by the significant and negative sign of the error correction term. In conclusion, the rapidly increasing house price is not caused by speculation activities in the housing market. Mor",33.0,https://www.emerald.com/insight/content/doi/10.1108/IJHMA-01-2018-0001/full/pdf?title=housing-prices-in-peninsular-malaysia-supported-by-income-foreign-inflow-or-speculation 85e7d045bbf2ae1d56d2757857ccc6cc136f63d1,10.52158/jacost.v4i1.491,"Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah",2023.0,Journal of applied computer science and technology,Evita Fitri,"The need for a place to live is one that many people prepare, both millennials and adults and the elderly. With the continued increase in population growth in Indonesia and increasing public interest in buying a place to live early on, this can make not all groups of people have a place to live or a house that is quite livable. Related to this, the public needs up-to-date information related to predictions of house prices both for housing and second-hand housing prices for planning purposes in the future. The purpose of this study is to carry out a comparative analysis of the prediction results of house prices with several Machine Learning algorithms consist of Linear Regression, Random Forest Regression and Gradient Boosted Trees Regression. Evaluation for all the method applying Cross-Validation. The evaluation is seen from the smallest Root Mean Square Error (RMSE) error rate of each testing method. The results of this study are the Random Forest Regression obtained an RMSE value of 0.440, the Linear Regression model obtained an RMSE value of 0.515 and the RMSE value of Gradient Boosted Trees Regression of 0.508. The results were obtained from testing a dataset of 2011 records w",33.0,https://journal.isas.or.id/index.php/JACOST/article/download/491/202 52c7622ab3374ca44aeb5523d520cbd8cf040cbf,10.3390/electronics11213448,Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia,2022.0,Electronics,Elham Alzain; A. Alshebami; Theyazn H. H. Aldhyani; S. N. Alsubari,"The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN mod",33.0,https://www.mdpi.com/2079-9292/11/21/3448/pdf?version=1666700097 071e12e47cb753c6fbc77c85f01df0ae2c5ee431,10.3390/ijgi8080349,Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning,2019.0,ISPRS Int. J. Geo Inf.,Xiao-Lu Zhou; Wei-Tian Tong; Dongying Li,"The rental housing market plays a critical role in the United States real estate market. In addition, rent changes are also indicators of urban transformation and social phenomena. However, traditional data sources for market rent prediction are often inaccurate or inadequate at covering large geographies. With the development of housing information exchange platforms such as Craigslist, user-generated rental listings now provide big data that cover wide geographies and are rich in textual information. Given the importance of rent prediction in urban studies, this study aims to develop and evaluate models of rental market dynamics using deep learning approaches on spatial and textual data from Craigslist rental listings. We tested a number of machine learning and deep learning models (e.g., convolutional neural network, recurrent neural network) for the prediction of rental prices based on data collected from Atlanta, GA, USA. With textual information alone, deep learning models achieved an average root mean square error (RMSE) of 288.4 and mean absolute error (MAE) of 196.8. When combining textual information with location and housing attributes, the integrated model achieved an a",33.0,https://www.mdpi.com/2220-9964/8/8/349/pdf?version=1564724590 d8a856187055cdc413865965b5224ffb0a84f023,10.3390/su7044524,"Determinants and Sustainability of House Prices: The Case of Shanghai, China",2015.0,,Lu-Yue Gao; Wing-Chau Kwong,"Recent housing policies include measures for home purchase control and shanty town redevelopment. This study proposes sustainable pricing, in that the long-run equilibrium price is determined by the fundamentals of house prices. We argue that changes in CPI might have led to rapidly growing house prices and rather high price levels. We investigate the long-run or short-run impacts of new commodity housing completions, transacted square meters of commodity housing, and CPI for house prices in Shanghai. We adopt monthly data for the period of 2005–2010. We test for unit roots using both the ADF and PP techniques and structural breaks using both the Zivot-Andrews (Model B) and Perron (Model C) methods. Considering Cheung-Lai and Reinsel–Ahn finite-sample corrections, the results suggest a long-run equilibrium. Housing completions negatively impact house prices in the short run. A positive volume-price relationship is suggested. Housing sales affect house prices in the short run but not vice versa. Hence, the empirical evidence supports the search model. In addition, CPI is strongly exogenous with respect to the long-run relationship and thus is a long-term determinant of house prices.",33.0,https://www.mdpi.com/2071-1050/7/4/4524/pdf?version=1429173742 fd8c84a49325bd0a9ce5d5d283038b1ef7c0d119,10.3390/ijerph18189534,Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level,2021.0,International Journal of Environmental Research and Public Health,Muhan Lv; Ning-Cheng Wang; S. Yao; Jian-Ping Wu; Lei Fang,"As vulnerable road users, elderly pedestrians are more likely to be injured in road crashes due to declining physical and perceptual capabilities. Most previous studies on the influence of the built environment on elderly pedestrian safety focused on intersections or areal units. Using a district of Shanghai as the study area, this research investigated the effects of the built environment at the road segment level with elderly pedestrian collision, taxi tracking point, point of interest, street view image, open street map, land use, housing price, and elderly population datasets. In particular, this research employed both Poisson and geographically weighted Poisson regression (GWPR) models to account for spatial nonstationarity. The Poisson model indicates that green space, sidewalks, and junctions on the roads significantly affected elderly pedestrian safety, and roads around nursing homes, schools, bus stops, metro stations, traditional markets, and supermarkets were hazardous for elderly pedestrians. The results of the GWPR model suggest that the influence of factors varied across the study area. Green space could decrease the risk of elderly pedestrian collisions only in areas",33.0,https://www.mdpi.com/1660-4601/18/18/9534/pdf?version=1631523474 28ae06c43aa39b15744b893b79ed7e473ecbb588,10.1016/j.jrurstud.2019.02.018,Exploring the gradient: The economic benefits of ‘nearby’ schools on rural communities,2019.0,Journal of Rural Studies,John Sipple; J. Francis; Peter C. Fiduccia,"Abstract Little is known about the socioeconomic impacts of the presence or absence of schools on local communities. In other words, are communities better off with or without a local school in or near its community center? In one of the few studies on this, Lyson (2002) argued that the presence of schools provides significant social and economic benefits to rural communities including higher property values and incomes. The main goal if this paper is to interrogate the area outside villages – what we measure as a 5 mile gradient or boundary. The data for this study are drawn from US Census TIGER files, American Community Survey data, and the NY State Department of Education including public school finance, demographic, enrollment, and performance data. In short, we are modeling the gradient around villages to see if village economic indicators vary with the concentration of public schools and the distance schools are from the center of the village. We find strong support for the assumption that schools are important to the economic vitality of rural communities and support for our method of geo-locating community institutions and measuring distance and concentration - what we term",33.0, d7f2c22f4dad1646cc637d46b60357c9328aff69,10.1109/snams52053.2020.9336575,Semantic Convolutional Neural Network model for Safe Business Investment by Using BERT,2020.0,"International Conference on Social Networks Analysis, Management and Security",Maryam Heidari; Setareh Rafatirad,"The real estate market creates one of the significant business domains for investors, but a wise investment in real estate is more important for low-income people who have just one home as their life-time investment. So during the economic recession, many homeowners lose their homes because of two major factors: one, they could not pay their mortgages, and second, the house could not be rented to cover the mortgages. So real estate investment can be secured if the house could be an excellent rental property. Information from real estate websites can be a rich source of knowledge essential to detect the house's potential as a rental property. This work uses a natural language processing approach to propose a new real estate investment model based on online textual information. For the first time, we apply a transfer learning approach based on multiple online resources to recognize the house as valuable rental property in the real estate market. Bidirectional attention models based on transformers (BERT) are used to extract features for semantic convolutional neural network models to secure real estate investment. This research has three main points: (1) using transformers to impleme",33.0, 6fd4947ce10bb6992fabea38096226a18d458d1c,10.1057/s41308-022-00185-5,When Does Monetary Policy Sway House Prices? A Meta-Analysis,2021.0,IMF Economic Review,Dominika Ehrenbergerová; Josef Bajzík; T. Havránek,"Several central banks have leaned against the wind in the housing market by increasing the policy rate preemptively to prevent a bubble. Yet the empirical literature provides mixed results on the impact of short-term interest rates on house prices: the estimated semi-elasticities range from $$-12$$ - 12 to positive values. To assign a pattern to these differences, we collect 1,555 estimates from 37 individual studies that cover 45 countries and 72 years. We then relate the estimates to 39 characteristics of the financial system, business cycle, and estimation approach. Our main results are threefold. First, the mean reported estimate is exaggerated by publication bias, because insignificant results are underreported. Second, inclusion of controls correlated with policy rates (credit or money supply) decreases the estimated effects of policy rates on house prices. Third, the effects are stronger in countries with more developed mortgage markets and generally later in the cycle when the yield curve is flat and house prices enter an upward spiral.",33.0,https://osf.io/npeqs/download c6ce97c5ea2cd6c9ad17a025928264a8da4c8daa,10.1177/23998083211029660,Using multi-source data to understand the factors affecting mini-park visitation in Yancheng,2021.0,Environment and Planning B Urban Analytics and City Science,C. Zhou; Leshan Fu; Yanwen Xue; Zhijie Wang; Yu Zhang,"Mini-parks are becoming a popular form of outdoor recreational space in densely populated areas, largely because their small size makes site selection easier than for ordinary parks. However, existing studies on mini-parks are limited because most of them rely on data collected through traditional surveys, which are severely restricted by space and time. In this study, we utilised Tencent user density data – a type of space–time synchronous data with high spatial resolution – to trace mini-park visitation in the main city of Yancheng, China, and we integrated data about land use, points of interest, transportation, demographics and housing prices to measure the parks’ surrounding features. We investigated how factors relating to the parks’ spatial and internal attributes, surrounding physical features and surrounding socio-economic features affected the number of park visits during the week versus during the weekend by establishing a series of multiple linear regression models. The results showed that higher resident population, more surrounding public toilets and larger open site promoted mini-park visits while distance to the city centre, surrounding large parks and main roads di",33.0, 45a596f462e06fb2c76f2a61093f529b8c17223a,10.1287/isre.2021.1044,The Societal Impact of Sharing Economy Platform Self-Regulations - An Empirical Investigation,2021.0,Information systems research,Wencui Han; Xun-Yi Wang; M. Ahsen; S. Wattal,"The rise of the sharing economy has disrupted traditional industries and has had many unforeseen societal impacts. This has sparked policy debates on whether and how the sharing economy should be regulated to promote the healthy growth of such markets. In this research, we examine the impact of platform self-regulations in the context of the home-sharing market. Using policy changes that reduce the number of Airbnb listings, we empirically test the impact of platform self-regulations on crime rates. Our results suggest that a reduction in Airbnb listings resulting from platform self-regulations leads to a reduction in crime. We further study the impact of these policy changes on different types of crime and find that these self-regulations lead to a reduction in incidents of crime such as assault, robbery, and burglary but an increase in theft incidents. In addition, we find that the impact of these policies varies based on the neighborhood’s characteristics, such as income, housing price, and population. This research contributes to our understanding of the societal impacts of the sharing economy and the impact of platform self-regulation. Our findings also provide empirical evide",33.0, 495dc31238bf58804ebc3129e90f840028c82038,10.1016/j.regsciurbeco.2015.02.005,The dynamics of house price responsiveness and locational sorting: Evidence from air quality changes,2015.0,,Corey Lang,"Despite extensive use of housing data to reveal valuation of non-market goods, the process of house price adjustment remains vague. Using the restricted access American Housing Survey, a high-frequency panel of prices, turnover, and occupant characteristics, this paper examines the time path of prices and preference-based sorting in response to air quality changes caused by differential regulatory pressure from the 1990 Clean Air Act Amendments. The results suggest that owner-occupied units capitalize changes quickly, whereas rent prices lag behind amenity levels. The delayed but sharp rent response temporally coincides with evidence of sorting, indicating a strong link between location choices and price dynamics.",33.0,https://digitalcommons.uri.edu/enre_facpubs/17 d81b33de36a00c54ba626dcf45a50f459a9a8c88,10.1016/0166-0462(92)90001-h,Market power of large cities and policy differences in metropolitan areas,1992.0,,W. Hoyt,"Abstract When a single city, the ‘central city’, has a large share of the metropolis population, it will influence housing prices in other, smaller cities, the ‘suburbs’. This market power leads to differences in government policy and property values between the central city and suburbs even when residents and amenities in the two regions are identical. When the central city's government is controlled by property-owning residents, its property tax rate exceeds the rate in the suburbs. The central city will also have lower property values than suburbs.",33.0,https://www.sciencedirect.com/science/article/pii/016604629290001H/pdf 9c71480b4307bcf9a70c22b2d0be8cfb0147a81e,10.1080/13683500.2019.1711027,Airbnb and VRBO: the impact of short-term tourist rentals on residential property pricing,2020.0,Current Issues in Tourism,D. Wyman; Chris Mothorpe; B. McLeod,"ABSTRACT The contemporary phenomenon of Airbnb, VRBO and peer-to-peer home sharing markets has generated considerable controversy regarding positive and negative community impacts. Our study examines the economic motives and costs of short-term rental properties. Using home sales data from the City of Isle of Palms, SC, we find short-term rental properties sell at a price premium relative to long-term rentals and owner-occupied properties providing confirmatory evidence for why residential property owners select into short-term residential property operation. We also find no pricing impact associated with proximity to short-term rental properties indicating homebuyers are not concerned with the perceived community nuisances. The positive economic return accruing to short-term rental properties suggests the short-term rental marketplace will continue to develop as a strategic competitor to traditional tourist lodging operators.",33.0, 555d702cde574ff00a5e1a9ed0511e4ab00f6e08,10.1109/icsai48974.2019.9010071,Prediction of House Price Based on The Back Propagation Neural Network in The Keras Deep Learning Framework,2019.0,International Conference on Systems and Informatics,Zhong-Yun Jiang; Guo-Xin Shen,"This paper uses the housing data of the chain home network to predict the price of second-hand housing in Shanghai. Firstly, this paper use the crawler technology to parse the URL text information through the j son request address and the BeautifulSoup parser. Then a multi-layer feedforward neural network model trained by error inverse propagation algorithm is established based on the deep learning library Keras. Finally, to enter standardized data to predict the price. The experimental results show that for the model with Gaussian noise, the sample with an absolute value of the relative error between the predicted value and the actual value is 95.59%. The model has a good effect in the house price forecast.",33.0, 9be16c1062e365367a91bac0d9c9590c43121d35,10.1177/0042098015606595,The spatial dimension of US house prices,2017.0,,Katharina Pijnenburg,,33.0,https://www.econstor.eu/bitstream/10419/215791/1/Pijnenburg-2015-The%20Spatial%20Dimension%20of%20US%20Ho%20-%20Kopie.pdf 8a8d2017d0b92a9ee363c4d5bd1da1d781f9babd,10.1016/j.landusepol.2018.09.011,Impact of an urban growth boundary across the entire house price spectrum: The two-stage quantile spatial regression approach,2019.0,,S. Mathur,,33.0, 321f85957cf306169b23c2f0c8a43bc39fd941f6,10.3390/land10121400,The Influence of Subjective and Objective Characteristics of Urban Human Settlements on Residents’ Life Satisfaction in China,2021.0,Land,Xueming Li; He Liu,"Under the guidance of people-oriented development concepts, improving residents’ life satisfaction has gradually become the goal of urban governance. Based on Chinese household tracking survey data and national socio-economic statistics, this study used the entropy method, multi-layer linear regression model and geographically weighted regression model and discusses the spatial heterogeneity of the impact of objective environmental characteristics and subjective perceived characteristics of urban residential environments on residents’ life satisfaction. It is of great importance to study the mechanisms through which subjective and objective characteristics of urban human settlements influence living satisfaction among residents. It is also important to discuss how to improve living satisfaction levels through the urban human settlements and to realize high-quality urban development. The research results show that in 2018, the overall level of life satisfaction among Chinese urban residents was relatively high. However, it is still necessary to continue to optimize the urban human settlements to improve residents’ life satisfaction. The objective characteristics of the urban human s",32.0,https://www.mdpi.com/2073-445X/10/12/1400/pdf?version=1640069186 d276c5c1f847d957f7143cdc67e6545f03d1f901,10.11113/mjfas.v16n2.1387,Review on Geographically Weighted Regression (GWR) approach in spatial analysis,2020.0,,A. Sulekan; Shariffah Suhaila Syed Jamaludin,"In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been successfully used in spatial analysis. GWR can theoretically integrate geographical location, altitude, and other factors for spatial analysis estimations, and reflects the non-stationary spatial relationship b",32.0,https://mjfas.utm.my/index.php/mjfas/article/download/1387/pdf 6e037a4be9101a30f19c9336371561161baba2f0,10.1002/ijfe.1570,Banking and Currency Crises: Differential Diagnostics for Developed Countries,2015.0,Social Science Research Network,Mark S. Joy; Marek Rusnák; Kateřina Šmídková; Bořek Vašíček,"We identify a set of ""rules of thumb"" that characterise economic, financial and structural conditions preceding the onset of banking and currency crises in 36 advanced economies over 1970–2010. We use the Classification and Regression Tree methodology (CART) and its Random Forest (RF) extension, which permits the detection of key variables driving binary crisis outcomes, allows for interactions among key variables and determines critical tipping points. We distinguish between basic country conditions, country structural characteristics and international developments. We find that crises are more varied than they are similar. For banking crises we find that low net interest rate spreads in the banking sector and a shallow or inverted yield curve are their most important forerunners in the short term, whereas in the longer term it is high house price inflation. For currency crises, high domestic short-term rates coupled with overvalued exchange rates are the most powerful short-term predictors. We find that both country structural characteristics and international developments are relevant banking crisis predictors. Currency crises, however, seem to be driven more by country idiosy",32.0,https://doi.org/10.2139/ssrn.2621627 961ef95d379df3b7d9c05cc6e318fdc1353e8f73,10.1080/01603477.2018.1486207,The drivers of household indebtedness reconsidered: An empirical evaluation of competing arguments on the macroeconomic determinants of household indebtedness in OECD countries,2018.0,Journal of post Keynesian economics,G. Moore; Engelbert Stockhammer,"Abstract Household debt is at a record high in most Organisation for Economic Co-operation and Development (OECD) countries and it played a crucial role in the recent financial crisis. Several arguments on the macroeconomic drivers of household debt have been put forward, and most have been empirically tested, albeit in isolation of each other. This article empirically tests 7 competing hypotheses on the macroeconomic determinants of household indebtedness together in one econometric study. Existing arguments suggest that residential house prices, upward movements in the prices of assets demanded by households, the income share of the top 1%, falling wages, the rolling back of the welfare state, the age structure of the population, and the short-term interest rate drive household indebtedness. We formulate these arguments as hypotheses and test them for a panel of 13 OECD countries over the period 1993–2011 using error correction models. We also investigate whether effects differ in boom and bust phases of the debt and house price cycles. The results show that the most robust macroeconomic determinant of household debt is real residential house prices, and that the phase of the deb",32.0,https://uwe-repository.worktribe.com/output/7450318 9cfd894dd64c5a4d6a62ed24cbf47908568dd49c,10.5198/jtlu.2018.1318,"Accessibility, urban form, and property value: A study of Pudong, Shanghai",2018.0,Journal of Transport and Land Use,Cheng-He Guan; Richard B. Peiser,"The effects of metro system development and urban form on housing prices highly depend on the spatial temporal conditions of the urban neighborhoods. However, scholars have not yet comprehensively examined these interactions at a neighborhood-scale. This study assesses metro access, urban form, and property value at both district- and neighborhood-levels. The study area is in Pudong, Shanghai where metro system development coincides with rapid urban growth. 279 neighborhoods are systematically and randomly selected for the district-level investigation and 31 neighborhoods selected from Pudong for neighborhood-level investigation. The analysis of variance shows that metro access is more positively correlated to property price in Pudong. The Pearson correlation, principle component, and ordinary least square regression analyses find that while accessibility attributes have positive influence on housing prices, neighborhood characteristics also exhibit pronounced impact on property price change over time. This study extends our knowledge on how metro system development interacts with land use efficiency; discusses planning policies corresponding to development stage that produce more ",32.0,https://www.jtlu.org/index.php/jtlu/article/download/1318/1162 9227a7e5ee91510694ed7aa9358e1709d0798b32,10.3846/1648715x.2004.9637518,A comparison between mra and rough set theory for mass appraisal. A case in bari,2004.0,,M. D'Amato,"Rough Set Theory is a property valuation methodology recently applied to property market data (d'Amato, 2002). This methodology may be applied in property market where few market data are available or where econometric analysis may be difficult or unreliable. This methodology was introduced by a polish mathematician (Pawlak, 1982). The model permit to estimate a property without defining an econometric model, although do not give any estimation of marginal or hedonic prices. I : ,he first version of RST was necessary to organize the data in classes before the valuation .The relationship between these classes defined if‐then rules. If a property belongs to a specific group then it will belong to a class of value. The relationship between the property and the class of value is dichotomous. In this paper will be offered a second version that improve the RST with a “value tolerance relation” in order to make more flexible the rule. In this case the results will come out from an explicit and specific relationship. The methodology has been tested on 69 transactions in the zone of Carrassi‐Poggiofranco in the residential property market of Bari.",32.0,https://journals.vgtu.lt/index.php/IJSPM/article/download/7298/6321 6f70b466e027fa246e430e1843fc722c6f677b3b,10.35957/jatisi.v8i1.701,Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression,2021.0,,A. Saiful,"A place to live or a house is one of the many primary needs for humans. Therefore it is very important to make a plan so that each family can have a private residence. This planning requires a prediction or forecast of future prices. So, the aim of this study is to create a house price prediction model using machine learning methods while the algorithm is linear regression. By doing web scraping to collect data, through several websites that are involved in the sale and purchase of houses. Meanwhile, according to home developers who were successfully asked in the field related to variables that affect house prices, including land area, standing building area, number of bedrooms, number of bathrooms, and the availability of a car park. To get a high predictive value, research is carried out repeatedly but the largest predictive value is using 80% of the dataset for training and 20% of the dataset is used for testing to produce an output value with an accuracy level of predicting 88%.",32.0,https://doi.org/10.35957/jatisi.v8i1.701 ada6c70891b2a3885f366b94109d59e0c4a2ca48,10.1111/coep.12105,Determinants of Financial Distress in Large Financial Institutions: Evidence from U.S. Bank Holding Companies,2016.0,,Zhi-Chao Zhang; Li Xie; Xiangyun Lu; Zhuang Zhang,"type=""main"" xml:id=""coep12105-abs-0001""> We investigate determinants of financial distress in large financial institutions based on the Distance-to-Default and Z-Scores measures. Using data of U.S. bank holding companies (BHCs), we find that the housing price index is a consistently significant factor across all BHCs and the non-performing loan ratio is the most powerful indicator for financial distress. Short-term wholesale funding is also a reliable default risk indicator. We additionally find that all the three regulatory capital requirements are very important for controlling default risk, particularly in the post-crisis period. (JEL C53, G14, G21, G28)",32.0,https://durham-repository.worktribe.com/output/1428145 6fc3727ce91fa0713c0d9f42c06f4f130629c6aa,10.1177/1078087420922910,Capitalizing on Collapse: An Analysis of Institutional Single-Family Rental Investors,2020.0,Urban Affairs Review,Gregg Colburn; Rebecca J. Walter; Deirdre Pfeiffer,"A well-documented consequence of the recent foreclosure crisis was a pronounced dislocation in the single-family home market. Large institutional buyers emerged to capitalize on this dislocation. These firms acquired hundreds of thousands of single-family homes to create a pool of single-family rentals (SFRs) in markets across the United States. Existing analyses of institutional investors focus on their aggregate characteristics and associated community effects, showing faster housing recovery in places hard hit by foreclosure but also increases in evictions and home prices. Relatively little is known about individual firms’ strategies or how they have evolved over time—knowledge that is critical to understanding the diversity of these actors and establishing causal links to community impacts. We help fill this gap by using quantitative and qualitative data from the corporate filings of publicly traded SFR companies to understand the industry’s birth, growth and development, strategies and approaches, and points of differentiation.",32.0, 40a8f9670773d4621da4506ed892504f82a9eaf6,10.1016/j.retrec.2018.07.006,The impact of metro accessibility on residential property values: An empirical analysis,2018.0,Research on Transport Economics,Li Zheng,"Abstract This paper investigates the influence of metro accessibility on Xi'an residential property values. The key finding is that proximity to metro stations tends to have a positive but nonlinear effect. Within two-kilometre road network distances from metro stations, property value uplift is lowest in areas immediately adjacent to metro stations, explained by negative externalities around metro stations outweighing accessibility gains. The highest effect is revealed at distances between 300 and 1200 metres away from stations. Other significant influences include the distance to the central business district, the size of the property, the age of the property, interior decoration, residential building type and geographical location. The evidence suggests that the catchment area size should be adjusted according to access mode shares. Policy implications are also provided for potential future decision making.",32.0, 38c0972e81f4719bfb5772adea20770439ebf55e,10.5089/9781451841756.001,Property Prices and Speculative Bubbles: Evidence from Hong Kong Sar,2000.0,Social Science Research Network,S. Kalra; D. Mihaljek; Christoph K. Duenwald,"This paper examines the determinants of residential property prices in Hong Kong SAR during 1980-98. It uses time-series analysis techniques to characterize price developments, establish empirical regularities, and provide measures of the deviations of actual price changes from trend. The analysis suggests that at the peak of the boom, in mid-1997, the level of property prices may have been 40-45 percent above levels suggested by developments in fundamentals. The analysis highlights the role of demand-side factors, and the data are not inconsistent with the notion that the property market may be subject to speculative bubbles.",32.0, 69c865e882cfde6eb8748b0af6993e4c4b6321df,10.1080/08941920601171998,Influence of Natural Amenities on Residential Property Values in a Rural Setting,2007.0,,E. White; L. Leefers,,32.0, dccf0ca9e91eb4d455d7811d94cd92a98df85076,10.15396/eres2002_106,The Determinants of House Price Dynamics,2002.0,,D. Capozza; C. Mack; P. Hendershott; C. Mayer,,32.0, 9ad67b728e57834df532ca3c3c2ceee7e418cb11,10.3390/land13040563,"Navigating Post-COVID-19 Social–Spatial Inequity: Unravelling the Nexus between Community Conditions, Social Perception, and Spatial Differentiation",2024.0,Land,Min-Jun Zhao; Ning Liu; Jin-Liu Chen; Danqing Wang; Peng-Cheng Li; Di Yang; Pu Zhou,"The 2023 SDGs report underscores the prolonged disruption of COVID-19 on community living spaces, infrastructure, education, and income equality, exacerbating social and spatial inequality. Against the backdrop of the dual impact of significant events and the emergence of digital technologies, a coherent research trajectory is essential for characterizing social–spatial equity and understanding its influential factors within the urban planning discipline. While prior research emphasized spatial dimensions and mitigated spatial differentiation to ensure urban equity, the complexity of these interconnections necessitates a more comprehensive approach. This study adopts a holistic perspective, focusing on the “social–spatial” dynamics, utilizing social perception (sentiment maps) and spatial differentiation (housing prices index) pre- and post-pandemic to elucidate the interconnected and interactive nature of uneven development at the urban scale. It employs a multi-dimensional methodological framework integrating morphology analysis of housing conditions, GIS analysis of urban amenities, sentiment semantic analysis of public opinion, and multiscale geographically weighted regression ",31.0,https://www.mdpi.com/2073-445X/13/4/563/pdf?version=1713795412 39cde0780a2ac86beb9466ecf9d772daed842f08,10.1108/jpif-08-2021-0073,Artificial intelligence algorithms to predict Italian real estate market prices,2021.0,Journal of Property Investment & Finance,Luca Rampini; Fulvio Re Cecconi,"PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations",31.0,https://re.public.polimi.it/bitstream/11311/1217365/5/Published_version.pdf 51ba82b66aef078b503b4cdb43476a9210154e7d,10.1016/j.heliyon.2023.e20408,Influence of urban forests on residential property values: A systematic review of remote sensing-based studies,2023.0,Heliyon,Ewane Basil Ewane; S. Bajaj; Luisa Velasquez-Camacho; Shruthi Srinivasan; Ju-Hyun Maeng; Anu Singla; Andreas Luber; S. de-Miguel; G. Richardson; E. Broadbent; A. Cardil; W. M. Jaafar; M. Abdullah; A. D. Corte; C. A. Silva; W. Doaemo; M. Mohan,"Urban forests provide direct and indirect benefits to human well-being that are increasingly captured in residential property values. Remote Sensing (RS) can be used to measure a wide range of forest and vegetation parameters that allows for a more detailed and better understanding of their specific influences on housing prices. Herein, through a systematic literature review approach, we reviewed 89 papers (from 2010 to 2022) from 21 different countries that used RS data to quantify vegetation indices, forest and tree parameters of urban forests and estimated their influence on residential property values. The main aim of this study was to understand and provide insights into how urban forests influence residential property values based on RS studies. Although more studies were conducted in developed (n = 55, 61.7%) than developing countries (n = 34, 38.3%), the results indicated for the most part that increasing tree canopy cover on property and neighborhood level, forest size, type, greenness, and proximity to urban forests increased housing prices. RS studies benefited from spatially explicit repetitive data that offer superior efficiency to quantify vegetation, forest, and tree",31.0,http://www.cell.com/article/S2405844023076168/pdf 4c9f15309d771ad882be1647ff76db97c439f8ae,10.17016/bulletin.2013.99-4,Mortgage market conditions and borrower outcomes: evidence from the 2012 HMDA data and matched HMDA–credit record data,2013.0,,Neil Bhutta; Glenn B. Canner,"This article describes mortgage lending activity in 2012 based on newly available data reported under the Home Mortgage Disclosure Act (HMDA). In addition, we present the results of a first look at a new data set composed of HMDA records matched to borrowers' credit records. Using the matched data, we compare borrowers’ credit characteristics at loan origination, and subsequent payment performance, by various HMDA attributes such as income, minority status, and type of lender. Also, because the credit record data are longitudinal and follow individuals rather than just their mortgages, we are able to study long-term outcomes of mortgage borrowers beyond performance on their mortgage, such as whether credit scores recover after delinquency at a different pace for different demographic groups. We focus on loans made in 2006, which reflect lending activity at the height of the housing boom, and loans made in 2010, which reflect the subdued market conditions post crisis. Key findings include: (1) substantive differences in performance across racial and ethnic groups exist even after controlling for credit score, house price appreciation, and higher-priced loan status; (2) credit score ",31.0,https://doi.org/10.17016/bulletin.2013.99-4 8465acc115a9287f1262d65c478ccb8a249aca9c,10.13060/00380288.2016.52.6.290,"Behind the Differentiation of Suburban Development in the Hinterland of Bratislava, Slovakia",2016.0,,M. Šveda; M. Madajová; P. Podolák,"Several tendencies seem to indicate that in the hinterland of Bratislava specifi c new residential zones are forming with a different socio-economic structure. This article analyses the hinterland of Bratislava from various perspectives of suburban development. The selection of variables covers the origin of in-migrants and their economic activity, education, and family status. Indicators of land use, housing construction, and property prices supplement these data. The data are processed based on a factorial ecology approach, which tries to discover the basic dimensions of the socio-spatial structure, and cluster analysis. Using these methods the authors identify similar clusters and categorise individual municipalities into relatively homogeneous units—suburban development types. The results lead to a number of very interesting fi ndings. The same types of suburban municipalities are not arranged in concentric zones, which means that the factor of distance from the city does not play a prominent role. The prevailing factor is the sectorial structure, which refl ects the different levels of attractiveness municipalities hold for different socio-economic groups of in-migrants. This ",31.0,https://doi.org/10.13060/00380288.2016.52.6.290 ba2856913112692e4760af3e13e02e28b6cf65bd,10.1016/j.jenvman.2023.119690,Unveiling spatial inequalities: Exploring county-level disaster damages and social vulnerability on public disaster assistance in contiguous US.,2023.0,Journal of Environmental Management,Yu Han; Haifeng Jia; Changqing Xu; M. Bockarjova; C. V. van Westen; L. Lombardo,"Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships between disaster damages, social vulnerability, and public disaster assistance in contiguous US counties from 2001 to 2021. LBMI results reveal that public disaster assistance has predominantly been directed towards post-disaster recovery efforts, with a particular focus on coastal communities affected by major declared disasters. However, the distributions of public assistance and individual housing assistance, which are the two primary sources of public disaster assistance, do not adequately cover physically and socially vulnerable communities. The distribution of pre-disaster risk mitigation also falls short of sufficiently covering vulnerable communities. Results further indicate the complex interactions between different categories of natural disasters and public assistances. The GWR model results demonstrate spatial variations in predic",31.0,https://doi.org/10.1016/j.jenvman.2023.119690 dc8e6c54148f21618cdfc6d8cb33a774e334d07d,10.3844/ajassp.2007.774.778,"The Influence of Consumers Behavior on the Variables Determining Residential Property Values in Lagos, Nigeria",2007.0,,M. Bello; V. Bello,"The objective of this study was to assess the influence of consumer behaviour on the variables determining residential property values in Lagos metropolis and how Nigerian valuers are incorporating these in their valuation exercise. The survey which was carried out within a period of three months (January, February and March 2006) involved buyers and valuers which participated in the sales of some properties within Lagos Metropolis. These buyers and valuers were asked to rank on a 4-point likert scale; the relative importance of each of the property attributes which affect the price of the property as perceived by them. The significance of each attribute was then assessed using Relative Importance Index (RII). The significant difference between the ranking of the buyers and valuers was assessed at each variable level and when all the variables were combined using the Kendall test of concordance and tested at .05 level of significant using chi- square approximation. Regressing the attributes of each property against the actual property paid, the significance of each attribute was determined; which was then compared with the ratings of both the valuers and the buyers. The study revea",31.0,https://doi.org/10.3844/ajassp.2007.774.778 c6ee4cf974aa13a6b6d37370b178e67c62cce7d1,10.2139/ssrn.2534538,Homeowner Balance Sheets and Monetary Policy,2014.0,,Aditya Aladangady,"This paper empirically identifies an important channel through which monetary policy affects consumer spending: homeowner balance sheets. A monetary loosening increases home values, thereby strengthening homeowner balance sheets and stimulating household spending due to a combination of collateral and wealth effects. The magnitude of these effects on a given household depends on local housing market characteristics such as local geography and regulation. Cities with the largest geographic and regulatory barriers to new construction see 3-4 percent responses in real house prices compared with unconstrained, elastic-supply cities where construction holds prices in check. Using non-public geocoded microdata from the Consumer Expenditures Survey, house price and consumption responses are compared across areas differing in local land availability and zoning laws to identify a marginal propensity to consume out of housing of 0.07. Homeowners with debt service ratios in the highest quartile have MPCs as high as 0.14 compared with negligible responses for those with low debt service ratios. This indicates a strong role for collateral effects, as opposed to pure wealth effects, in driving t",31.0,https://doi.org/10.17016/feds.2014.98 9c388fa2f01b7841befae3fc169896157e7e24ee,10.3390/su14073977,Macroeconomic Determinants of Household Debt in OECD Countries,2022.0,Sustainability,B. Dumitrescu; A. Enciu; C. Hândoreanu; Carmen Obreja; Florin Blaga,"This article investigates the macroeconomic determinants of household debt in developed economies using a sample comprising 26 OECD countries for the period of 2002q1–2020q4. By resorting to the unconditional quantile regression, we find relevant asymmetries in the response of household debt. According to our results, economic growth leads to lower household debt, but the beneficial effect decreases as the level of household debt increases. Inflation lowers household borrowing only if the level of debt is high. Higher house prices lead to higher household debt, with the impact becoming stronger as the level of debt is higher. Investments go hand in hand with household debt, and higher investments lead to higher levels of borrowing, even when household debt is already high. Mortgage credit interest rates are positively linked with household debt, starting with higher debt levels. A rising unemployment rate leads to lower household debt, but the link becomes weaker as the level of debt increases. Higher public expenditures are generally associated with lower household debt. In addition, we find that household debt exhibits very powerful autoregressive behavior, being difficult to red",31.0,https://www.mdpi.com/2071-1050/14/7/3977/pdf?version=1648533076 8c46154fbaf164447e17b77d9874009c161a116a,10.5198/jtlu.2018.1096,The effect of light rail transit service on nearby property values: Quasi-experimental evidence from Seattle,2018.0,,Michael R. Ransom,"This paper examined the effect of the construction of light rail transit stations on surrounding residential property values in Seattle, Washington. It studied sales of homes in the areas around the seven stations that serve primarily residential areas in the Rainier Valley, using a difference-in-differences regression technique to obtain plausibly causal estimates of the effect of new rail service. For these seven stations, the estimated impact of light rail service was positive for only one station and negative for two stations. Estimated impacts for the other stations were small and statistically insignificant. These results suggest that light rail service did not provide value to the neighborhoods in the Rainier Valley of Seattle. I speculate that the transport service provided by light rail was not a significant improvement relative to the bus lines that serviced the area before light rail was built.",31.0,https://www.jtlu.org/index.php/jtlu/article/download/1096/1027 f47ecd2b33bc55cd0bbd405e4c5792a7ecd9786b,10.1177/23998083211001836,Assessing the impacts of Airbnb listings on London house prices,2021.0,Environment and Planning B Urban Analytics and City Science,James A. Todd; A. Musah; Jenny Cheshire,"Over the course of the last decade, sharing economy platforms have experienced significant growth within cities around the world. Airbnb, which is one of the largest and best-known platforms, provides the focus for this paper and offers a service that allows users to rent properties or spare rooms to guests. Its rapid growth has led to a growing discourse around the consequences of Airbnb rentals within the local context. The research within this paper focuses on determining impact on local housing prices within the inner London boroughs by constructing a longitudinal panel dataset, on which a fixed and random effects regression was conducted. The results indicate that there is a significant and modest positive association between the frequency of Airbnb and the house price per square metre in these boroughs.",31.0,https://doi.org/10.1177/23998083211001836 eb9bf90ff4d6cbe6b02d0574c1bc5e009a0d67e5,10.1007/s00181-019-01638-z,Does the rental housing market stabilize the economy? A micro and macro perspective,2019.0,Empirical Economics,Micha L. Rubaszek; M. Rubio,"The size of the rental housing market in most countries around the globe is small. In this article, we claim that this may be detrimental to macroeconomic stability. We do it in three steps. First, using survey data for Poland, a country with a high homeownership ratio, we discuss microeconomic housing tenure choice determinants. Second, with a panel of 28 EU countries over the period 2004–2017, we provide evidence that the response of house prices to macroeconomic fundamentals is attenuated by the size of the private rental market. Third, we propose a DSGE model in which households satisfy housing needs both by owning and by renting. By simulating the model, we show that reforms enhancing the rental housing market contribute to macroeconomic stability. We conclude by formulating policy recommendations.",31.0,https://link.springer.com/content/pdf/10.1007/s00181-019-01638-z.pdf f7ceff5e47104285f6ef7df62cbbeb8ee1d0fc56,10.1108/pm-05-2019-0025,"What is determining affordable housing shortages in the Greater Kuala Lumpur, Malaysia?",2019.0,Property Management,Abdul Lateef Olanrewaju; A. Idrus,"PurposeThe purpose of this paper is to investigate the determinants of the affordable housing shortage in the Greater Kuala Lumpur from the suppliers’ perspectives.Design/methodology/approachPrimary data were collected through a cross-sectional survey questionnaire comprising 21 determinants and 111 experts in the housing industry.FindingsThe affordable housing shortages are consequences of regulations and policies on land allocations, building materials and the affordable housing market. The government should provide more lands to the developers or the government should directly build affordable housing on their lands. To lower the cost of construction, the government should reduce the importation tax and procedures, and the housing industry should find alternative building materials.Originality/valueTheoretically, the research provided fresh insights into the causes of housing shortages and reasons for the increase in housing prices. The results will be useful to policymakers towards affordable housing delivery and to the developers and contractors on measures to increase profit margins and increase housing supply.",31.0, a81cee106f6de923f245809c00295e4d754aa08d,10.1016/j.cities.2019.05.008,Children's education or parents' employment: How do people choose their place of residence in Beijing,2019.0,Cities,Zhen-Shan Yang; P. Hao; Dian-Hua Wu,"Abstract Residential mobility has become an increasingly important issue in China, where urban families participate frenetically in the commodity housing market leading to soaring property prices. Applying place dependence approach and logistic regression models, this paper examines the association between residential mobility intentions and place dependence for employment and education in Beijing. The results reveal that people's intentions to move homes are driven by place dependence on the access to high-quality schools for their children, while the dependence for employment is rather weak. The findings indicate children's education has emerged as a new driving force that shapes urban space in contemporary China and therefore should be a focal point for planners and policy makers in urban resource distribution. This paper provides evidences to further our understanding of the high premium of properties that enable access to good schools. The needs for other spatial utilities and sentimental attachment to a place also encourage residential mobility intentions, which deserves further research.",31.0, f551b1ea9b4f3dbf7359755fbb74d57e2bc1d550,10.1016/j.jfe.2016.09.001,The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity,2016.0,,James J. Yoo; R. Ready,,31.0, 59a5f8bcac2543d148400d9a905209afac109dde,10.1108/ijhma-07-2021-0083,Housing prices in emerging countries during COVID-19: evidence from Turkey,2021.0,International Journal of Housing Markets and Analysis,M. Kartal; Serpil Kılıç Depren; Özer Depren," Purpose By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks. Design/methodology/approach A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness. Findings Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decrea",30.0, aface46f4a7d8b6117a46fb9f7c1f4b36ebff6d8,10.3390/land10080871,"Assessing the Spatial Distribution Pattern of Street Greenery and Its Relationship with Socioeconomic Status and the Built Environment in Shanghai, China",2021.0,Land,Chao Xiao; Qian Shi; Chengcheng Gu,"Urban greenery is widely acknowledged as a key element for creating livable urban environments and improving residents’ quality of life. However, only a few current studies on the subject of urban greenery focus on a human visual perspective and take street greenery into consideration. Street greenery is an indispensable component of urban vegetation to which residents have a higher frequency of access. Additionally, few studies focused on the disparity of the green view at a micro-level, such as at a county or community level. This study explored the spatial distribution of street greenery and its influential factors using the green view index (GVI) as the main evaluation indicator. Compared to other traditional indicators of greenery, such as the normalized difference vegetation index (NDVI) and accessibility, GVI is recognized as a human-oriented indicator to evaluate the quantity of greenery viewed by human eyes in daily life. The downtown area of Shanghai was chosen as the case study, as it reflects the common phenomenon of street greenery in many megacities globally. In addition, county/jiedao (the same administrative area as county in China) level was selected as the minimum",30.0,https://www.mdpi.com/2073-445X/10/8/871/pdf?version=1629385221 5c49ee64a013a601696109dca1f94cfa85559ac9,10.3390/su9112050,Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China,2017.0,,Sheng-Fu Yang; Shou-Geng Hu; Wei Li; Chuan-Rong Zhang; J. Torres,"With the rapid development of land marketization in China, the spatial patterns of residential land prices in different regions have become increasingly complicated. The very high and continuously rising residential land prices in many cities are causing significant challenges to economic development and social stability. Yet, there has only been a limited amount of attempts made to model and analyze the regional dynamic changes of residential land price systematically, especially in term of the spatially varying effects of key demographic and economic factors. In this study we provided a perspective analysis of the changes of residential land prices in 2008, 2011 and 2014 based on the land price monitoring records of 105 cities and then conducted a geographically weighted regression (GWR) analysis on the relationships between residential land price and three major impact factors (i.e., immigrant population, gross domestic product (GDP) and investment in residential buildings). Results show that the areas in which GDP had relatively strong positive impacts on residential land price expanded with time. The negative effects of immigrant population on residential land price were mainl",30.0,https://www.mdpi.com/2071-1050/9/11/2050/pdf?version=1510729192 a9d2f6b4566a69dd064ab165369c22f6cba032ad,10.1177/0003122418808212,The Social Ecology of Speculation: Community Organization and Non-occupancy Investment in the U.S. Housing Bubble,2018.0,American Sociological Review,Adam Goldstein,"The housing boom of the mid-2000s saw the widespread popularization of non-occupant housing investment as an entrepreneurial activity within U.S. capitalism. In 2005, approximately one sixth of all mortgage-financed home purchases in the United States were for investment purposes. This article develops a sociological account that links the geographic distribution of popular investment to the social and institutional organization of communities. Regression analyses of 1,566 municipalities from 2000 to 2006 indicate that non-occupant investment (but not conventional owner-occupant investment) occurred at significantly greater rates in places where local development institutions were organized in accordance with laissez-faire principles to a greater degree, where economic activities in other domains were less locally embedded in place, and where greater residential instability produced more tenuous connections between residents and places. The magnitudes of these associations suggest that social organization and cultural-institutional conditions were at least as informative as variations in housing price appreciation in shaping the incidence of investor activity during the bubble. Soc",30.0,https://journals.sagepub.com/doi/pdf/10.1177/0003122418808212 414fccdeb81b4c74fecec8265eb146b06dc6c1c5,10.3390/ijgi11080450,"House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China",2022.0,ISPRS Int. J. Geo Inf.,Zimo Wang; Yicheng Wang; Sen-Sen Wu; Zhen-Hong Du,"Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographical neural network-weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13",30.0,https://www.mdpi.com/2220-9964/11/8/450/pdf?version=1661353256 c06d5f638d16e50d415bef8ac040c45cf86c66f3,10.4314/afrrev.v5i5.13,Determinants of Residential Property Value in Nigeria – A Neural Network Approach,2011.0,,So Igbinosa,"This study investigated, by means of artificial intelligent system, the influence of residential real estate property characteristics on property values (prices) in Nigeria, using two major cities (Benin and Lagos) as examples. It revealed a high positive linear correlation between property characteristics and the property market values; an indication that these characteristics reasonably predict property market values. The study demonstrated that although several property characteristics can be identified with residential real estate properties, only are few important ones have significant impact on the market values of such properties. It identified nine (9) property characteristics that have relatively strong impact on market values (prices) and to that extent influence the sales and purchase decisions of sellers and buyers in Nigeria. The results of the study should enable Real Estate Professionals to make fair estimates of the market values of residential real estate properties given the features/characteristics of such housing units. This would aid rapid valuation, help to improve housing quality and make possible mass evaluation of properties. The study recommends to real es",30.0,https://www.ajol.info/index.php/afrrev/article/download/72305/61234 efc4e0e8447790429f5c3ab259369c72dea903c3,10.3390/buildings7030067,An Analysis of the Influence of Property Tax on Housing Prices in the Apulia Region (Italy),2017.0,,F. Tajani; P. Morano; C. Torre; F. Di Liddo,"In this research, the socio-economic determinants of housing market values have been examined, highlighting their respective contributions to the formation of the property prices and, in particular, verifying the property tax liability. The property tax is a factor that could determine, through modest fluctuations, the revival of the property demand, generating positive effects also on the construction sector, which has been currently affected by the negative real estate contingency. The functional correlations of housing prices with the main socio-economic variables considered (i.e., housing rents, household incomes, household consumptions, property taxes, population and mean population age) have been explained through an econometric analysis implemented with an innovative methodology that uses multi-objective genetic algorithms. The analysis is contextualized to the Apulia region in the South of Italy, and the population of the sample studied consists of 258 individuals, corresponding to the municipalities of the region. In particular, the data collected for each variable considered refer to two different moments, that is, the year 2011 and the year 2015, in order to take into ac",30.0,https://www.mdpi.com/2075-5309/7/3/67/pdf?version=1501157469 6102a582e5b767e292fa8b423e39a575e103fab4,10.1109/access.2024.3484409,Explainable and Fair AI: Balancing Performance in Financial and Real Estate Machine Learning Models,2024.0,IEEE Access,D. Acharya; B. Divya; Karthigeyan Kuppan,"This paper introduces a framework that integrates fairness and transparency into advanced machine learning models, specifically LightGBM and XGBoost, applied to loan approval and house price prediction datasets. The key contribution is using fairness-focused techniques, such as Calibrated Equalized Odds and Intersectional Fairness, which are not widely studied in financial and real estate contexts. To improve model transparency, SHAP (SHapley Additive exPlanations) is utilized along with a novel fairness-based interpretability method to measure both model fairness and the importance of individual features. Through comprehensive experiments, we show that LightGBM delivers high accuracy while balancing fairness and performance effectively. The broader relevance of this work is discussed in the context of governance and regulatory requirements, highlighting the importance of responsible practices in high-stakes financial decision-making processes. This research highlights the importance of fairness and transparency in real-world applications, promoting equity, trust, and adherence to evolving legal standards, and provides practical insights for data scientists, machine learning resear",30.0,https://doi.org/10.1109/access.2024.3484409 6c357f0b55230fac9254e6f78ac6b621e03f6295,10.1155/2022/5750354,Research on Prediction and Analysis of Real Estate Market Based on the Multiple Linear Regression Model,2022.0,Scientific Programming,Guang-Fu Liu,"Since the housing reform in the 1990s, China’s real estate market has expanded and developed rapidly. It has quickly become the pillar of China’s national economy and made a great contribution to China’s economic growth. However, China’s real estate market started late and its development is not perfect, and house prices show obvious volatility. Accurate prediction of house prices is conducive to the government issuing appropriate regulatory policies, helping investors formulate correct investment strategies, and guiding the healthy and long-term development of the real estate market. Based on the statistical data of the real estate market, this study analyzes the influencing factors of the real estate price, establishes a multiple linear regression model, uses the least square method to solve the unknown parameters of the model, and finally constructs a house price prediction model to predict and analyze the real estate market. The results show that the maximum error of the real estate price prediction model is no more than 8%. The above results show that the house price prediction model has high accuracy and can predict house prices accurately and efficiently.",30.0,https://downloads.hindawi.com/journals/sp/2022/5750354.pdf 91d62e0a4389bf301996a5500bb0d179c175b868,10.1177/0042098020940596,"Does demand lead supply? Gentrifiers and developers in the sequence of gentrification, New York City 2009–2016",2020.0,,Kasey Zapatka; Brenden Beck,"Consumption-side theorists of gentrification examine the flow of middle-class White people into previously working-class neighbourhoods and argue that their demand for housing stimulates gentrification. In contrast, production-side theorists emphasise the movement of capital into previously disinvested neighbourhoods and contend that profit-seeking development increases property values and sparks gentrification. Hybrid theorists argue that consumption and production occur simultaneously. This article operationalises arguments made by each approach, and asks: Do gentrifiers precede rising home values or do rising home values precede gentrifiers? To answer this question of sequence, we build a dataset of census and property tax assessment data for 2192 New York City census tracts between 2009 and 2016. Using cross-lagged regression models with tract and year fixed effects, we find neighbourhoods that experienced an increase in White, middle-class residents had related housing price spikes in each of the subsequent two years. A 1% increase in gentrifiers was associated with a subsequent 2.7% increase in property values. However, housing market growth did not predict future increases i",30.0, 5799e9fdfeeb5491696b33a90d8820a1cd036992,10.1177/0042098019841580,Time-varying spillovers among first-tier housing markets in China,2020.0,Urban studies,Chien-Fu Chen; Shu-hen Chiang,"Numerous efforts have over the last few years been devoted to studying spillovers (ripple effects) among cities as a means of evaluating overheated housing markets. What seems to be lacking, however, is the application of a rolling-window approach to further explore time-varying spillovers in a timely manner in order to look more closely at a housing market with Chinese characteristics; for example, a market with rapidly increasing prices and a sequence of policy recommendations. By focusing on total, directional and net spillovers, and using 2000–2017 monthly housing price data across six Chinese cities, this study’s results indicate that time-varying spillovers provide a better understanding of the interactions among first-tier cities. It is interesting to note that, following the downside risk faced by the economy in 2014, the spillovers among cities have been abruptly transformed into those exhibiting bilateral co-movements based on high total spillovers and low net spillovers, and these results are also confirmed by the frequency dynamics of spillovers. Based on the above, there is sufficient evidence to conclude that the housing frenzies in China, which have become a national",30.0, ae31923538a592d9fb50b45411104605766cdb3c,10.63642/1357-1419.1165,The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal,2014.0,Journal of Property Tax Assessment & Administration,P. Bidanset; J. Lombard,"This research, using valid sales of single-family homes in Norfolk, Virginia, from 2010 to 2012, evaluated the varying predictability power that different kernel and bandwidth specifications in geographically weighted regression models lend to mass appraisal of real estate, and the potential improvement each lends to taxing entities in attaining equity, uniformity, and ultimately defensibility in their property assessments. Specifically the weighting specifications that were studied were the Gaussian kernel with fixed bandwidth, the Gaussian kernel with adaptive bandwidth, the bi-square kernel with fixed bandwidth, and the bisquare kernel with adaptive bandwidth. The model applying a Gaussian kernel and adaptive bandwidth produced results that were most uniform by IAAO standards.",30.0,https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1027&context=publicservice_pubs 6622f2e193dd9097a182b85d88899a024114da2c,10.1016/j.ecosys.2017.05.010,House price convergence in euro zone and non-euro zone countries,2018.0,Economic Systems,I. Tsai,"This study investigates whether single currency use increased house price convergence among various countries. First, the panel unit root test results indicate that the house prices in euro zone countries were more correlated than the house prices in non-euro zone countries. Second, the house prices in various European countries converged towards the house prices in Germany, which uses the single currency, rather than towards those in the United Kingdom, indicating that single currency use increased the influence of the German housing market on other markets. Finally, the log t regression model, a new convergence test, was employed and determined that the house prices in various European countries were not converging before 1992 but began to do so after that year. After the euro was implemented as an official currency, the house prices in various countries converged towards a consistent level. On the basis of the relative transition paths, this study determines that the differences among housing markets in various countries have continuously decreased since 1992. The empirical results indicate that the law of one price is applicable to tradable goods and that single currency use ca",30.0, 47f4f12dfdc2f2a2f52d8186c0612144f9e4cf3d,10.1016/j.regsciurbeco.2017.03.007,A spatial panel data model with time varying endogenous weights matrices and common factors,2017.0,Regional Science and Urban Economics,Wei Shi; Lung-Fei Lee,"Many spatial panel data sets exhibit cross sectional and/or intertemporal dependence from spatial interactions or common factors. In an application of a spatial autoregressive model, a spatial weights matrix may be constructed from variables that may correlate with unobservables in the main equation and therefore is endogenous. Some common factors may be unobserved and correlate with included regressors in the equation. This paper presents a unified approach to model spatial panels with endogenous time varying spatial weights matrices and unobserved common factors. We show that the proposed QML estimator is consistent and asymptotically normal. As its limiting distribution may have a leading order bias, an analytical bias correction is proposed. Monte Carlo simulations demonstrate good finite sample properties of the estimators. This model is empirically applied to examine the effects of house price dynamics on reverse mortgage origination rates in the United States.",30.0, 85ae2e999ee3123df9539e7486aa7f970eefb096,10.1177/0042098019844410,Housing wealth and household consumption in urban China,2020.0,Urban studies,Zekai He; Jingjing Ye; Xiuzhen Shi,"Using a large Chinese household survey data set, we investigate the effect of home value appreciation on urban household consumption. The paper identifies the causal effect of housing wealth using both Two-Stage Least Squares (2SLS) estimates and regression discontinuity designs. The research demonstrates that the housing boom in China has resulted in higher consumption and that a 10% increase in home wealth raises overall consumption by approximately 3%. The average marginal propensity to consume out of housing wealth is about 5 cents with substantial heterogeneity across household characteristics. The findings highlight the role of mortgage debts, health insurance, education and risk preference in explaining the variation in household consumption. Our results persist when we adopt different proxies for housing wealth.",30.0, c61a2e9cb48d9ce2388c57c7f6481ae03e737367,10.3390/rs14225695,"Analyzing the Spatially Heterogeneous Relationships between Nighttime Light Intensity and Human Activities across Chongqing, China",2022.0,Remote Sensing,Jihao Wu; Yue Tu; Zuo-Qi Chen; Bai-Lang Yu,"Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that ",29.0,https://www.mdpi.com/2072-4292/14/22/5695/pdf?version=1668149059 b83f17a7e1eef4fc1b79f9414b714ff69928fd00,10.1016/j.envint.2022.107592,Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models.,2022.0,Environment International,H. Ohanyan; L. Portengen; O. Kaplani; A. Huss; G. Hoek; J. Beulens; J. Lakerveld; Roel C. H. Vermeulen,"BACKGROUND Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies. METHODS Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables. RESULTS Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in th",29.0,https://ars.els-cdn.com/content/image/1-s2.0-S0160412022005190-ga1_lrg.jpg 858c68df89105f4baacaa7fae3ecc94beef7a4c9,10.3390/land10090993,Spatiotemporal Evolution and Driving Forces of Sustainable Development of Urban Human Settlements in China for SDGs,2021.0,Land,Xueping Cong; Xueming Li; Yilu Gong,"As the world’s largest developing country, China has actively implemented the UN Sustainable Development Goals (SDGs). Sustainable development of urban human settlements is the result of localization and the deepening of sustainable development theory in China. This study combines SDGs to construct an evaluation index system for the sustainable development of urban human settlements in China, using optimization methods, such as natural breaks (Jenks), exploratory spatial data analysis, and GeoDetector, to conduct systematic research on the spatiotemporal evolution of the current sustainable development level and analyze the core driving forces of urban human settlements in 285 prefecture-level cities in China from 2000 to 2019. Our study revealed that: (1) The overall sustainable development level of urban human settlements and their subsystems in China has improved steadily, but the levels of subsystems are quite different; (2) the sustainable development level of the urban human settlements in China can be expressed as a spatial pattern of “high in the east and low in the west, high in the south and low in the north” and has relatively significant spatial correlation characterist",29.0,https://www.mdpi.com/2073-445X/10/9/993/pdf?version=1632385600 636533598466dd14611cefe29abe82583f3f96dc,10.3390/atmos7070088,Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model,2016.0,,Tian-Hao Zhang; W. Gong; Zhongmin Zhu; Kun Sun; Yu-Si Huang; Yu-Xi Ji,"The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acc",29.0,https://www.mdpi.com/2073-4433/7/7/88/pdf?version=1467027449 e342be7c20edf948522fe1ed024f7034d77728e1,10.3390/ijerph13100974,Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model,2016.0,International Journal of Environmental Research and Public Health,Tian-Hao Zhang; Gang Liu; Zhongmin Zhu; W. Gong; Yu-Xi Ji; Yu-Si Huang,"The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has bee",29.0,https://www.mdpi.com/1660-4601/13/10/974/pdf?version=1475229093 aefc018d01de446c186b7a6a23197cd5ab8fbf0d,10.1016/j.jclepro.2019.117884,Promoting green residential buildings by increasing homebuyers’ willingness to pay: Evidence from Sino-Singapore Tianjin Eco-city in China,2019.0,Journal of Cleaner Production,Yun-Xia Liu; Xuan Sun; T. Sun; Xun-Peng Shi; Jun-Min Liu,"Abstract Increasing willingness to pay (WTP) is critical to promote green residential buildings (GRBs), but residents' WTP for GRBs has not yet been well studied. To examine residents' WTP for GRBs and its determinants, a survey was conducted among 511 current GRB occupants living in Sino-Singapore Tianjin Eco-city in China, and latent class regression was used to analyze the heterogeneity of their preferences. Four factors of residential satisfaction were identified—operation and maintenance, comfort and health, architectural and construction quality, and accessibility of GRBs—and these determinants of WTP had different effects on different groups of residents. The mean WTP of the majority (64.5%) of residents was 374 CNY/m2 (about 54 USD), accounting for only 2.2% of the housing price. Four segments of residents with heterogeneous preferences were identified. The dominant segment (accounting for 64.5%) was “Indifferent residents with low WTP”. The second largest segment (accounting for 18.9%) was “Critical residents with 1000 + WTP”, followed by the third largest segment (13.3%) — “High WTP residents driven by comfort and health”, while the smallest segment (3.3%) was “Operation ",29.0,https://opus.lib.uts.edu.au/bitstream/10453/138564/4/Preprint%20for%20public.pdf 9508228bb2b44c5093951691be8bee91f5a675f1,10.1177/14673584211057568,Analyzing the influence of short-term rental platforms on housing affordability in global urban destination neighborhoods,2022.0,Tourism and Hospitality Research,L. Garay-Tamajón; Josep Lladós-Masllorens; Antoni Meseguer-Artola; S. Morales-Pérez,"In the last decade, global urban destinations have witnessed an unprecedented wave of tourism growth based on the development of short-term rental platforms (STAP) and of a particular platform, Airbnb. Literature has demonstrated this growth’s influence in different contexts, highlighting its impact on housing markets, mostly at the city level and through correlation or regression analyses. This study goes a step further, focusing on the neighborhood level for the case of Barcelona (Spain) and using k-means clustering. Results show how the concentration of Airbnb listings in “highly touristified” and “trendy” neighborhoods has been associated with an increase in rental prices in these areas. Moreover, the subsequent increase in the demand for tourism-related services in these neighborhoods has led to a displacement of residents to peripheral neighborhoods, which has also put pressure on these areas’ housing prices. These processes have been accompanied by an increasing reaction from local anti-tourism movements. Based on these findings, we suggest that global urban destinations managers need to manage STAP and tourism-related service development not only in relation to the tourism ",29.0,http://hdl.handle.net/10609/146268 ab5906aebca54d560975ed8377c154784102848e,10.1371/journal.pone.0190566,Poverty concentration in an affluent city: Geographic variation and correlates of neighborhood poverty rates in Hong Kong,2018.0,PLoS ONE,Ying-Qi Guo; Shu-Sen Chang; F. Sha; P. Yip,"Previous investigations of geographic concentration of urban poverty indicate the contribution of a variety of factors, such as economic restructuring and class-based segregation, racial segregation, demographic structure, and public policy. However, the models used by most past research do not consider the possibility that poverty concentration may take different forms in different locations across a city, and most studies have been conducted in Western settings. We investigated the spatial patterning of neighborhood poverty and its correlates in Hong Kong, which is amongst cities with the highest GDP in the region, using the city-wide ordinary least square (OLS) regression model and the local-specific geographically weighted regression (GWR) model. We found substantial geographic variations in small-area poverty rates and identified several poverty clusters in the territory. Factors found to contribute to urban poverty in Western cities, such as socioeconomic factors, ethnicity, and public housing, were also mostly associated with local poverty rates in Hong Kong. Our results also suggest some heterogeneity in the associations of poverty with specific correlates (e.g. access to h",29.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0190566&type=printable 80fe158f19eef03fed332e44b8d5645af1a5f086,10.1016/j.cities.2019.05.011,The long- and short-run influences of housing prices on migration,2019.0,Cities,Chien-Wen Peng; I. Tsai,"Abstract A change in housing prices has a profound impact on households' housing equity and future moving decisions. While most previous studies focus on discussing the mobility lock-in effect due to housing price depreciation, revealing that there is a positive relationship between housing prices and migration, this study reexamines their relationship by using the panel cointegration method and city-level panel data for Taiwan during the 1994–2016 period. The empirical results reveal that migration and housing prices are cointegrated, and the influence of housing prices on migration is significantly positive in the long run. However, the influence of housing price changes on migration is not as significant as expected in the short run. To further examine their short-run relationships, we use quantile regression and the results show that the influence of housing price changes on migration is significantly negative below the 0.5 quantile, but it turns out to be significantly positive in the 0.9 quantile. The influence of housing price changes on migration is not significant between the 0.5 and 0.8 quantiles. We conclude that the influence of housing price changes on migration might ",29.0,https://www.sciencedirect.com/science/article/pii/S0264275118311776/pdf 77edf6f3233b35bde823e71bdf993fb172c3fb34,10.3390/su12051844,"The Spatial Optimization and Evaluation of the Economic, Ecological, and Social Value of Urban Green Space in Shenzhen",2020.0,Sustainability,Yu-Han Yu; Wenting Zhang; Peihong Fu; Wei Huang; Ke-Ke Li; K. Cao,"Urban green space (UGS) is important in urban systems, as it benefits economic development, ecological conservation, and living conditions. Many studies have evaluated the economic, ecological, and social value of UGS worldwide, and spatial optimization for UGS has been carried out to maximize its value. However, few studies have simultaneously examined these three values of UGS in one optimization system. To fill this gap, this study evaluated the economic value of UGS in terms of promoting housing prices, its ecological value through the relief of high land surface temperature (LST), and its social value through the provision of recreation spaces for residents within a 255 m distance. Subsequently, these three values were set as objectives in a genetic algorithm (GA)-based multi-objective optimization (MOP) system. Shenzhen was taken as the case study area. The results showed that the influencing distance of UGS in Shenzhen for house prices was 345 m, and the influencing distance of UGS for LST was 135 m. Using MOP, the Pareto solutions for increasing UGS were identified and presented. The results indicate that MOP can simultaneously optimize UGS’s economic, ecological, and socia",29.0,https://www.mdpi.com/2071-1050/12/5/1844/pdf?version=1584010003 7f557028a6a440495332f1291c4bc86bf98e207a,10.3390/ijgi8100431,Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing,2019.0,ISPRS Int. J. Geo Inf.,Shiwei Zhang; L. Wang; Feng Lu,"In China, the housing rent can clearly reveal the actual utility value of a house due to its low capital premium. However, few studies have examined the spatial variability of housing rent. Accordingly, this study attempted to determine the utility value of houses based on housing rent data. In this study, we applied mixed geographically weighted regression (MGWR) to explore the residential rent in Nanjing, the largest city in Jiangsu Province. The results show that the distribution of residential rent has a multi-center group pattern. Commercial centers, primary and middle schools, campuses, subways, expressways, and railways are the most significant influencing factors of residential rent in Nanjing, and each factor has its own unique characteristics of spatial differentiation. In addition, the MGWR has a better fit with housing rent than geographically weighted regression (GWR). These research results provide a scientific basis for local real estate management and urban planning departments.",29.0,https://www.mdpi.com/2220-9964/8/10/431/pdf?version=1569727755 3d11d31fcae498dbd6d0ec3938ff0ec4a665dc5f,10.1016/j.econmod.2021.105595,Club convergence in European housing prices: The role of macroeconomic and housing market fundamentals,2021.0,Economic Modelling,L. Maynou; Mercedes Monfort; Bruce Morley; Javier Ordóñez,"Abstract This study aims to determine whether there is convergence in house prices across a sample of European economies, including members and non-members of the Eurozone. Overall the literature has found no evidence of convergence of house prices across Europe. Using data from twelve countries over the time period 2004Q2 to 2016Q3 we test for the null hypothesis of convergence against an alternative of divergence (or club convergence) and apply a Bayesian dynamic panel model to determine house price dynamics and drivers. The results suggest the presence of five housing market clubs across Europe but there is no evidence of a Eurozone club. In addition we find that the main determinants of real house prices are mainly fiscal factors and unemployment. Given the significance of housing markets to the economy and stability of the financial system, the Eurozone may wish to consider measures that facilitate convergence across members’ housing markets.",29.0,https://purehost.bath.ac.uk/ws/files/222039432/EU_House_pricesP.pdf 9e58ea178e1a91acb1ed38fcd0d0e1c584035198,10.1080/17441730.2020.1781380,Factors delaying marriage in Korea: an analysis of the Korean population census data for 1990–2010,2020.0,,Bun Lee; Jennifer L. Klein; M. Wohar; Sang-seog Kim,"ABSTRACT The Korean total fertility rate is one of the lowest in the world. This study assesses a broad range of factors associated with delayed first marriages, an important determinant of low fertility, using the Korean Population Census 2 per cent sample from 1990, 1995, 2000, 2005 and 2010 and other data sources. Using a multivariate logistic regression, we examine the probability of ever being married for men and women aged 30–34 and 35–39 for different years. We find that women with higher education delay marriage compared to those with a high school degree. Highly educated men marry earlier than men with high school or less education, particularly those aged 35–39. The strongest gender effect occurs among those working in agriculture, a declining industry in Korea. Men working in agriculture are much less likely to marry by age 40, but women marry earlier. In 2005 and 2010, men working in stable, high-paying industries marry earlier, with similar effects for women aged 30–34. Finally, several regional characteristics are associated with delayed marriage including higher housing prices, higher income for women, lower unemployment rate for young women, and higher income inequa",29.0, 7548ae3e2da742d93482dc814c7cf13fa9d2ec46,10.20852/ntmsci.2018.327,A new machine learning approach to house price estimation,2018.0,New Trends in Mathematical Sciences,ChangchunWang Wang; Hui Wu,"In this paper, we propose using Random forests, a machine lea rning approach, to build house price estimation model. Compared to benchmark linear regression model, random fore sts model can better capture hidden nonlinear relations bet ween the price and features of houses and gives an overall better estimatio n. Numerical experiments have been done on North Virginia ho use price data, which can strongly demonstrate our findings.",29.0,https://doi.org/10.20852/ntmsci.2018.327 dd4c845a6fa1712ce6f79824837b5f6b8278689d,10.1016/j.landusepol.2020.104828,Impact of transit stations on house prices across entire price spectrum: A quantile regression approach,2020.0,,S. Mathur,"Abstract This study provides evidence on the impact of a heavy-rail-based transit station on house prices by employing quantile regression method on a dataset that inventories sales transactions of single-family houses during the January 2000–April 2018 period, and within eight kilometers (five miles) of the Warm Springs station of the San Francisco Bay Area Rapid Transit system. The station is located in Fremont, CA. The results show that a) the station increased house prices across the entire price spectrum, and b) the price increase began more than ten years before the rail service commenced. On a larger note, this study should strengthen efforts to a) provide or extend heavy-rail-based rapid transit systems in areas of high need and b) explore the use of value capture tools to fund transit.",29.0, efc50e4c353becbdc49355ba841dc94648fbb8f4,10.1080/17421772.2021.1905173,Women’s political empowerment and welfare policy decisions: a spatial analysis of European countries,2021.0,Spatial Economic Analysis,R. De Siano; Valentina Chiariello,"ABSTRACT This study analyses the impact of female political empowerment on European countries’ welfare policies. Following a spatial econometric approach, the results reveal the presence of a spatial dependence across countries and a consistent gender effect. In particular, the descriptive representation seems to favour a higher distribution of resources to sectors in which women are disproportionately active (health, family, housing, elderly care). Moreover, the study reveals positive spatial spillovers arising from women’s political empowerment in neighbouring countries that suggest the existence of an active and visible role model crossing national jurisdictions. These outcomes are robust to different metrics of neighbourliness.",29.0, ef5c7069d1e5054563b1e1ad22a5d9cd55c2bb54,10.1177/0042098012474699,"Spatial Impacts, Local Labour Market Characteristics and Housing Prices",2013.0,,Liv Osland; I. Thorsen,,29.0,http://hdl.handle.net/11250/225721 35d47b5d82e1cdb4d70de7bf35cdfe970d793f35,10.2139/ssrn.1316046,Machine Learning and the Spatial Structure of House Prices and Housing Returns,2008.0,,A. Caplin; S. Chopra; J. Leahy; Yann LeCun; Trivikraman Thampy,,29.0,http://yann.lecun.com/exdb/publis/pdf/caplin-ssrn-08.pdf 50988fbd4988f4591a49222eaa562f537f0a0053,10.3390/ijgi10110791,Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression,2021.0,ISPRS Int. J. Geo Inf.,Xinyu Qu; Xin-Yan Zhu; Xiong-Wu Xiao; Hua-Yi Wu; Bingxuan Guo; De-Ren Li,"Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affect",28.0,https://www.mdpi.com/2220-9964/10/11/791/pdf?version=1637318215 b215af9f8ae710a24bab775329367e43d0fcdb80,10.1016/j.apgeog.2019.102141,Spatial inequality of housing value changes since the financial crisis,2020.0,,Han-Duo Li; Y. Wei,"Abstract The financial crisis has dramatically reshaped the map of inequality; in particular, wealth has been redistributed because of fluctuations in the prices of equities and housing. However, since the explanation of this issue has by default been seen as economists' responsibility, the spatial dimension of the financial crisis still remains unexplored, especially at the intra-urban level. This study examines the local geographies of the housing value bust (2008–2012) and boom (2012–2016) since the financial crisis, with an explicit emphasis on their impacts on urban inequality in Salt Lake County, Utah. We find that housing value changes differ across space and appear strongly associated with the spatial distribution of neighborhood conditions and urban amenities. Additional regressions confirm that a city's housing market volatility is amplified by uneven distribution of physical and service amenities and residential segregation. Moreover, the significance level of local attributes changes over the bust-boom cycle of the housing market. The comparison between bust and boom models suggests that the value of houses enjoying shade from trees and proximity to jobs, places of wors",28.0,https://doi.org/10.1016/j.apgeog.2019.102141 82ff2adbafcb1eb044009ead08e899c359026383,10.3390/land10030317,"Spatio-Temporal Non-Stationarity and Its Influencing Factors of Commercial Land Price: A Case Study of Hangzhou, China",2021.0,Land,Zhuoma Garang; Ci-Fang Wu; Guan Li; Y. Zhuo; Zhong-Guo Xu,"Investigating the characteristics and mechanisms of the spatial and temporal variations of commercial land prices and its major subdivisions has great theoretical and practical significance in the study of urban economy and its spatial refinement management. Unlike general commodity prices, land prices are influenced by geographical location and tend to fluctuate over time. However, most scholars have not explored the influence mechanism of commercial land prices in both time and space. To help bridge this gap, this study takes the sample commercial land prices in the main urban area of Hangzhou from 2006 to 2015 as the empirical research object and investigates the spatiotemporal evolution mechanism of urban commercial land prices through a comparative analysis of the multiple regression analysis (MRA) with ordinary least squares (OLS), the geographically weighted regression (GWR), the temporally weighted regression (TWR), and the geographically and temporally weighted regression (GTWR) models. Results indicate that the land prices of land for financial facilities (Commercial Land Category 1) and commercial-business land (Commercial Land Category 2) in Hangzhou show different spat",28.0,https://www.mdpi.com/2073-445X/10/3/317/pdf?version=1616652616 e3bf74e07e1abec913fc027b14a4a1cbbbc9b105,10.1038/s41598-024-68536-y,Understanding spatial inequalities and stratification in transportation accessibility to social infrastructures in South Korea: multi-dimensional planning insights,2024.0,Scientific Reports,Sang-Wan Lee; Junhyuck Im; Kuk Cho,"This research investigated spatial inequalities in transportation accessibility to social infrastructures (SIs) in South Korea, using a multi-dimensional methodological approach, including descriptive/bivariate analysis, explanatory factor analysis (EFA), K-Mean cluster analysis, and multinomial logit model (MNL). Our study confirmed pronounced spatial disparities in transportation accessibility to SIs, highlighting significantly lower access in rural and remote regions compared to urban centers and densely populated areas, consistent with existing literature. Building on prior findings, several additional findings were identified. First, we uncovered significant positive correlations among accessibility to different types of SIs in four critical categories: green and recreation spaces, health and aged care facilities, educational institutions, and justice and emergency services, revealing prevalent spatial inequality patterns. Second, we identified three distinct accessibility clusters (High, Middle, and Low) across the critical SI categories. Specifically, residents within the High cluster benefited from the closest average network distances to all SIs, while those in the Low clu",28.0,https://doi.org/10.1038/s41598-024-68536-y 78d8bb54c29683e66b89b9728165167874c24ad3,10.1038/s41597-022-01857-7,The PAD-US-AR dataset: Measuring accessible and recreational parks in the contiguous United States,2022.0,Scientific Data,M. Browning; A. Rigolon; S. Ogletree; Ruo-Yu Wang; J. Klompmaker; Christopher Bailey; R. Gagnon; P. James,"Most spatial epidemiological studies of nature-health relationships use generalized greenspace measures. For instance, coarse-resolution spatial data containing normalized difference vegetative index (NDVI) values are prominent despite criticisms, such as the inability to restrain exposure estimates to public and private land. Non-threatening natural landscapes can improve health by building capacities for health-promoting behaviors. Recreational and accessible parks may best activate such behaviors. We curated the Parks and Protected Areas Database of the U.S. (PAD-US) to identify parks that are accessible for outdoor recreation. Our title adds “AR” to “PAD-US” where A = Accessible and R = Recreational. We validated the PAD-US-AR by comparisons with greenspace datasets and sociodemographics, which demonstrated its uniqueness from other commonly employed metrics of nature exposure. The PAD-US-AR presents reliable estimates of parks in the contiguous U.S. that are accessible for outdoor recreation. It has strong associations with home prices, shares of female residents, and shares of older residents. This dataset can accompany other nature exposure metrics in environmental epidemiol",28.0,https://www.nature.com/articles/s41597-022-01857-7.pdf 21f24a1900db79f54627e571805048eaeef71844,10.3390/land9050143,Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area,2020.0,Land,Dai-Kun Wang; V. Li; Hua-Yi Yu,"The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R2 of 0.8192, performs better in the mass appraisal modeling of real estate. The comparison of different models provides a useful benchmark for policy makers regarding the mass appraisal process of urban centers. The finding also highlights the spatial characteristics",28.0,https://www.mdpi.com/2073-445X/9/5/143/pdf?version=1588928173 50ccaf235841000469c1c97d10a59e9d15aa811e,10.2139/ssrn.1327079,The Jumbo-Conforming Spread: A Semiparametric Approach,2008.0,,S. Sherlund,"This paper estimates the jumbo-conforming spread using data from the Federal Housing Finance Board's Monthly Interest Rate Survey from January 1993 to June 2007. Importantly, this paper augments the typical parametric approach by adding state-level foreclosure laws and ZIP-level demographic variables to the model, estimating the effects of loan size and loan-to-value ratio on mortgage rates nonparametrically, and including geographic location as a control for some potentially unobserved borrower and market characteristics that might vary over geography, such as credit scores, debt-to-income ratios, and house price volatility. A partial locallinear regression approach is used to estimate the jumbo-conforming spread, on the premise that loans similar to each other in terms of loan size, loan-to-value ratio, or geographic location might also be similar in other, unobservable borrower and market characteristics. I find estimates of the jumbo-conforming spread of 13 to 24 basis points - 50 to 24 percent smaller since about 1996, when credit scores became widely used in mortgage underwriting, than estimates from a commonly used parametric model. I therefore attribute the difference in es",28.0,https://doi.org/10.17016/feds.2008.01 f070dde7bc852cd2a59b47a93eac509f011b8b76,10.1016/j.enpol.2023.113425,Shedding light on large-scale solar impacts: An analysis of property values and proximity to photovoltaics across six U.S. states,2023.0,Energy Policy,S. Elmallah; B. Hoen; K. Fujita; Dana Robson; Eric Brunner,"We examine the impact of large-scale photovoltaic projects (LSPVPs) on residential home prices in six U.S. states that account for over 50% of the installed MW capacity of large-scale solar in the U.S. Our analysis of over 1,500 LSPVPs and over 1.8 million home transactions answers two questions: (1) what effect do LSPVPs have on home prices and (2) does the effect of LSPVP on home prices differ based on the prior land use on which LSPVPs are located, LSPVP size, or a home’s urbanicity? We find that homes within 0.5 mi of a LSPVP experience an average home price reduction of 1.5% compared to homes 2–4 mi away; statistically significant effects are not measurable over 1 mi from a LSPVP. These effects are only measurable in certain states, for LSPVPs constructed on agricultural land, for larger LSPVPs, and for rural homes. Our results have two implications for policymakers: (1) measures that ameliorate possible negative impacts of LSPVP development, including compensation for neighbors, vegetative shading, and land use co-location are relevant especially to rural, large, or agricultural LSPVPs, and (2) placeand project-specific assessments of LSPVP development and policy practices ar",28.0,https://doi.org/10.1016/j.enpol.2023.113425 245c544deb37ae431fc42a9673596d8aff1015e1,10.3390/geosciences9030143,Estimating Residential Property Values on the Basis of Clustering and Geostatistics,2019.0,Géosciences,B. Całka,"The article presents a two-stage model for estimating the value of residential property. The research is based on the application of a sequence of known methods in the process of developing property value maps. The market is divided into local submarkets using data mining, and, in particular, data clustering. This process takes into account only a property’s non-spatial (structural) attributes. This is the first stage of the model, which isolates local property markets where properties have similar structural attributes. To estimate the impact of the spatial factor (location) on property value, the second stage involves performing an interpolation for each cluster separately using ordinary kriging. In this stage, the model is based on Tobler’s first law of geography. The model results in property value maps, drawn up separately for each of the clusters. Experimental research carried out using the example of Siedlce, a city in eastern Poland, proves that the estimation error for a property’s value using the proposed method, evaluated using the mean absolute percentage error, does not exceed 10%. The model that has been developed is universal and can be used to estimate the value of ",28.0,https://www.mdpi.com/2076-3263/9/3/143/pdf?version=1553416075 2d313697303a7e43de30379a88a43861c16a920e,10.1016/j.envsci.2018.03.020,A participatory sustainability assessment for integrated watershed management in urban China,2018.0,Environmental Science and Policy,Daniele Brombal; Yuan Niu; L. Pizzol; Angela Moriggi; Jing-Zhi Wang; A. Critto; Xia Jiang; Bei-Bei Liu; A. Marcomini,"Abstract This paper introduces a participatory framework for Sustainability Assessment (SA) for urban Integrated Watershed Management (IWM). The framework is applied to the Lihu Lake Basin (Wuxi City), where between 2002 and 2012 an IWM program was implemented, coordinating water environmental management measures with urban planning. The framework for SA introduced in this paper is based on a Multi-Criteria-Decision-Analysis (MCDA) approach integrating criteria of environmental, economic, and social sustainability. Local stakeholders were engaged in focus group discussions (FGDs) to validate and weigh criteria and attributes employed in the SA framework. Results of the model application indicate that the programme implemented in the Lihu Basin yielded positive results in enhancing environmental conditions, providing more sustainable avenues of environmental management funding, and fostering economic growth. Despite efforts put forward by local authorities, performance of social indicators was comparatively worse, due to relocation policies, increase in housing prices, and scarce public participation. Results show that decision makers pursued viability, rather than comprehensive sus",28.0,https://iris.unive.it/bitstream/10278/3700200/6/Brombal%20et%20al_Env%20Sci%20and%20Pol_submitted%20version.pdf 3f22184169e17efdde4d547ac74eb66e4ac2f628,10.1016/j.cities.2020.102963,A sociotechnical approach to causes of urban blight using fuzzy cognitive mapping and system dynamics,2021.0,,A. Lousada; F. Ferreira; I. Meidutė-Kavaliauskienė; Ronald W. Spahr; Mark A. Sunderman; L. Pereira,"Abstract Urban blight often is prominent in urban environments. It consists of real estate degradation to the extent that blighted residential properties, unfortunately, may provide housing only for the poorest social classes. Urban blight reduces real property values in urban neighborhoods and urban centers, reduces business investment and increases unemployment. Urban blight is not static, but continuously changing, either getting worse or improving. Thus, it is an issue of growing concern for urban decision makers who may attempt to determine its causes. Determining causes of urban blight facilitates the development of strategic measures for the prevention and potential eradication of this phenomenon. This study seeks to bolster the identification of factors that cause urban blight and makes important contributions to both the academic communities and society at large. By combining fuzzy cognitive mapping and the system dynamics approach, our proposal facilitates the identification of urban blight determinants and the development of alternatives to control and eradicate them. Data used in our study were provided by a panel of urban blight experts, and allowed for the identificat",28.0, 26160ee6ff482780c9d31e920e68be1d1afe8d27,10.2139/ssrn.2376447,Macroeconomic Determinants of Retirement Timing,2013.0,Social Science Research Network,Y. Gorodnichenko; Jae G. Song; D. Stolyarov,"We analyze lifetime earnings histories of white males during 1960-2010 and categorize the labor force status of every worker as either working full-time, partially retired or fully retired. We find that the fraction of partially retired workers has risen dramatically (from virtually 0 to 15 percent for 60-62 year olds), and that the duration of partial retirement spells has been steadily increasing. We estimate the response of retirement timing to variations in unemployment rate, inflation and housing prices. Flows into both full and partial retirement increase significantly when the unemployment rate rises. Workers around normal retirement age are especially sensitive to variations in the unemployment rate. Workers who are partially retired show a differential response to a high unemployment rate: younger workers increase their partial retirement spell, while older workers accelerate their transition to full retirement. We also find that high inflation discourages full-time work and encourages partial and full retirement. Housing prices do not have a significant impact on retirement timing.",28.0,https://doi.org/10.2139/ssrn.2376447 f497cbcdac077d815a27baedf430241efe9e0e8b,10.1007/s11146-009-9187-x,The Effect of House Prices on Household Consumption in Italy,2009.0,,R. Calcagno; E. Fornero; M. Rossi,"This paper studies the effect of a change in real estate wealth on the consumption behaviour of Italian households, using the Bank of Italy’s Survey of Household Income and Wealth dataset. We relate annual household consumption to capital gains in housing, controlling for characteristics such as age. In line with the empirical predictions of our model, we find the oldest households—which are less affected by the higher costs of future rent—to be the most affected by increases in real net housing wealth. Younger households, on the other hand, are not significantly affected in their consumption decisions by house price increases. We also take into account the fact that benefiting from capital gains is conditional on owning housing wealth and estimate the different impacts of house price changes on the savings behaviours of both homeowners and renters. Our estimates suggest that house price increases raise consumption not only for homeowners but also for renters.",28.0,https://link.springer.com/content/pdf/10.1007/s11146-009-9187-x.pdf adf56929a00d06585be1de8b25cb19aa042a377a,10.1109/icsdm.2015.7298035,Comparing multilevel modelling and artificial neural networks in house price prediction,2015.0,International Conference on Spatial Data Mining and Geographical Knowledge Services,Yingyu Feng; Kelvyn Jones,,28.0,https://research-information.bris.ac.uk/en/publications/4cb022a6-f18c-4859-819a-f562397648bc 4c4eada45b39b2e42049dde5414739efc4157af8,10.1108/ijhma-10-2015-0065,Impact of crime on spatial analysis of house prices: evidence from a UK city,2016.0,,David McIlhatton; W. McGreal; Paloma Taltavull de La Paz; A. Adair,,28.0,https://rua.ua.es/dspace/bitstream/10045/66092/5/2016_McIlhatton_etal_IJHMA_accepted.pdf 490d4d485dd5637fff425cc76757634968be50d7,10.1016/j.procs.2022.09.078,Machine learning in house price analysis: regression models versus neural networks,2022.0,International Conference on Knowledge-Based Intelligent Information & Engineering Systems,I. Foryś,,28.0,https://www.sciencedirect.com/science/article/pii/S1877050922009516/pdf 23a44dc59a57633db481bb6c2791607cd9db4d67,10.1108/ijhma-09-2012-0043,Understanding rental prices in the UK: a comparative application of spatial modelling approaches,2014.0,,M. McCord; P. Davis; M. Haran; David McIlhatton; John McCord,,28.0, 386cac1e6715dcdd12706d7a0a2b7a86aa891511,10.1111/1540-6229.12300,A Meta‐Analysis of School Quality Capitalization in U.S. House Prices,2019.0,Real Estate Economics,G. Turnbull; Minrong Zheng,,28.0, 9b509546df1d755957e76825929b8262de870534,10.1016/j.frl.2023.104212,The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis,2023.0,Finance Research Letters,Jin Shao; Jingke Hong; Xian-Zhu Wang; Xiaochen Yan,,28.0, 9c4bc8b56ec39ddc9c64518ff5ba94a0c504e862,10.1109/tencon.2016.7848726,Predicting public housing prices using delayed neural networks,2016.0,IEEE Region 10 Conference,Lipo Wang; F. Chan; Yaoli Wang; Qing Chang,,28.0, 63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,10.1080/2167034x.2003.12461361,"External Factors, Housing Values, and Rents: Evidence from Survey Data",2000.0,Journal of Housing Research,Vedia Dökmeci; Zeynep Önder; Abdullah Yavas,"This study uses a rich survey data to analyze housing values and rents in Istanbul, Turkey. In addition to variables related to location, household characteristics, and the physical characteristics of the property, the data involves a number of “qualitative” questions related to a set of external factors, such as the satisfaction level of the occupants with green area, parks, recreational facilities, transportation, distance to work, and noise. The paper identifies and compares the set of external factors that have a significant impact on property values and rents. Introduction When valuing residential property, one must consider the effects of the external factors as well as the characteristics of the property itself. Typically, these external factors are grouped together under a “location” variable. The principal reason for this practice is that the available data sets, such as the Multiple Listing Data sets, usually do not provide information on individual external factors. The other reason is that most of these external factors, such as noise, view, greenery, and accessability, are difficult to measure. The current study overcomes this problem by generating the data through an ",27.0,https://repository.bilkent.edu.tr/bitstreams/0b6eff2a-fd50-4d66-ad90-7e49de9d8dd1/download ab724aa7119a7690563e5882fcfdcb67432c2d6a,10.1080/16549716.2020.1785737,Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions,2020.0,Global Health Action,M. H. Dangisso; D. Datiko; B. Lindtjørn,"ABSTRACT Background Previous studies from Ethiopia detected disease clustering using broader geographic settings, but limited information exists on the spatial distribution of the disease using residential locations. An assessment of predictors of spatial variations of TB at community level could fill the knowledge gaps, and helps in devising tailored interventions to improve TB control. Objective To assess the pattern of spatial distribution of pulmonary tuberculosis (PTB) based on geographic locations of individual cases in the Dale district and Yirga Alem town in southern Ethiopia. Methods The socio-demographic characteristics of PTB cases were collected using a structured questionnaire, and spatial information was collected using geographic position systems. We carried out Getis and Ord (Gi*) statistics and scan statistics to explore the pattern of spatial clusters of PTB cases, and geographically weighted regression (GWR) was used to assess the spatial heterogeneities in relationship between predictor variables and PTB case notification rates (CNRs). Results The distribution of PTB varied by enumeration areas within the kebeles, and we identified areas with significant hotspot",27.0,https://www.tandfonline.com/doi/pdf/10.1080/16549716.2020.1785737?needAccess=true 56386c52394e63b253a32f5213486cab6b5ef66c,10.1108/ijhma-03-2020-0030,"Young adults and homeownership in Jakarta, Indonesia",2020.0,,R. Abidoye; Gitta Puspitasari; R. Sunindijo; M. A. Adabre," Purpose Homeownership, especially for young adults, is a significant challenge in nearly every country and Indonesia, the fourth most populous country in the world, is not exempted. Its capital city, Jakarta, has the lowest homeownership rate when compared with other cities and if this challenge remains unresolved, it could lead to more social and economic issues in the country. Hence, this study aims to investigate the homeownership of young adults in Jakarta, focussing on young adults’ opinions, perceptions and experiences regarding homeownership opportunities. Design/methodology/approach A questionnaire survey was conducted to collect data from young adults in the study area. The collected data were analysed using the statistical package for the social sciences 24.0 software. Descriptive analysis, Cronbach’s alpha test, Pearson’s correlation test and mean score ranking were adopted to analyse the collected data. Findings The result shows that homeownership is driven by factors that are more functional and realistic (in terms of a place to live, marriage and parenthood) rather than those related to pride or social status representation (as a personal or career accomplishment",27.0,https://unsworks.unsw.edu.au/bitstreams/bc3df575-27dc-47d3-92f1-5a8616e3856c/download 721bde276de0a5af885925a9444c628cfce6d18a,10.5198/jtlu.2019.1310,The impacts of light rail on residential property values in a non-zoning city: A new test on the Houston METRORail transit line,2019.0,Journal of Transport and Land Use,Qi-Sheng Pan,"The impacts of rail transit system on residential property values have been examined for many metropolitan areas in the U.S. But there are few studies on the effects of light rail in a non-zoning city. As the rail transit in the largest non-zoning city, Houston’s light rail transit line, or the so-called METRORail, has not received much attention from the planning research society since it opened to the public in 2004. A previous study by the author utilized 2007 household data to analyze the impacts of Houston’s METRORail line and found the net effects of the rail transit line change significantly at different distances from the rail stations. One limitation of that study was that the physical environment and neighborhood characteristics of the station areas may not have had notable changes over a relatively short time span, i.e., three years after the opening of the light rail. This study employs 2010 InfoUSA household data to re-examine the effects of Houston’s METRORail line. Similar to the previous studies, the author adopts a traditional ordinary linear regression (OLS) to investigate the contribution of a set of variables representing the physical, neighborhood, and accessib",27.0,https://www.jtlu.org/index.php/jtlu/article/download/1310/1234 e55da2524b1dacf953ec82b23999593d88674d3d,10.3390/su11072030,Do Sociodemographic Characteristics in Waste Management Matter? Case Study of Recyclable Generation in the Czech Republic,2019.0,Sustainability,K. Rybová,"The generation of recyclables in the Czech Republic has long been under the European average, but the proportion from municipal waste as a whole has been growing over the past few years. Previous research in the Czech Republic mainly focused on organizational or situational factors explaining recycling performance in municipalities. This study focuses on individual characteristics that are connected, among other things, to ongoing demographic changes. Currently ongoing sociodemographic development in the Czech Republic, as well as other developed countries, influence a broad range of aspects of social life, including waste generation and its structure. This paper aims at quantifying the relation between the sociodemographic characteristics of municipality inhabitants and recyclable generation. For this purpose, 13 variables describing inhabitants, households, and housing in 4897 Czech municipalities were selected that could influence the generation of recyclables according to foreign studies. Data were analyzed using multidimensional linear regression. Even though the resulting model only explains 9%, it is statistically significant and implies that sociodemographic variables can h",27.0,https://www.mdpi.com/2071-1050/11/7/2030/pdf?version=1554450413 7334365aa3d0db3c210dbd2d0f20f8963faab418,10.1016/j.jamda.2020.08.011,Developments in the Market for Assisted Living: Residential Care Availability in 2017,2020.0,Journal of the American Medical Directors Association,Portia Y. Cornell; Wen-Han Zhang; L. Smith; Shekinah Fashaw; K. Thomas,"Objectives: Describe how the availability of assisted living (AL) and dementia-specific AL vary across counties and correlate with demographic and socioeconomic characteristics. Design: Maps, univariate statistics, and standardized mean differences show the differences between counties with high and low levels of AL market penetration, and between counties with and without dementia-specific AL. Setting and participants: Data collected from state agencies on licensed AL communities, capacity, and geographic location, and population characteristics from the Area Health Resource file. We include novel and previously undescribed data on dementia-specific AL licenses in 21 states. Measures: AL market penetration is reported as the number of AL units or beds per 1,000 persons over age 65 in a county. Results: In comparison to counties with the lowest AL penetration, high-penetration counties had higher high school and college education attainment (mean 25.3% vs. 18.5%) and median annual income ($56,000 vs. $46,800), and lower poverty (12.8% vs. 17.3%) and unemployment rates (3.9% vs. 5.1%). Compared to counties with AL but no dementia-specific care, counties with dementia care had substa",27.0, 3244a7cf83af83a35bb79ecb76bce4a294ab683b,10.1016/j.jhe.2018.05.002,Credit conditions and the housing price ratio: Evidence from Ireland’s boom and bust,2018.0,Journal of Housing Economics,Ronan C. Lyons,"The Great Recession starting in 2007 has refocused attention on the importance of understanding housing market dynamics as contributors to macroeconomic fluctuations. While the sale-to-rent ratio of housing prices is generally regarded as a fundamental barometer of housing market health, the study of its determinants remains in its infancy. This paper examines the housing price ratio in Ireland since 2000, a period including an extreme housing market cycle. Using new data on first-time buyer loan-to-value ratios, a one-step error correction model of the housing price ratio in Ireland is presented for the first time. It finds clear evidence that, alongside user cost, credit conditions were central in determining equilibrium in the housing market. Throughout, and especially earlier in the sample, there is rapid adjustment of the housing price ratio to its implied equilibrium relation. There is evidence that the housing market regime changed during the period, in 2010 and again in 2014/2015. The preferred specifications imply that a ten percentage point increase in the median first-time buyer loan-to-value was associated with a 9% rise in sale prices, holding other factors – including",27.0,https://www.sciencedirect.com/science/article/pii/S1051137717300372 08d28e648021ede36f2afd59780f7283afbd0212,10.1021/acs.est.0c01175,"Toward Accurate, Policy-Relevant Fossil Fuel CO2 Emission Landscapes.",2020.0,Environmental Science and Technology,K. Gurney; Yang Song; Jian-Ming Liang; G. Roest,"The bottom-up (BU) approach has been used to develop spatiotemporally resolved, sectorally disaggregated fossil fuel CO2 (FFCO2) emission data products. These efforts are critical constraints to atmospheric assessment of anthropogenic fluxes in addition to offering the climate change policymaking community usable information to guide mitigation. In the United States, there are two high-resolution FFCO2 emission data products, Vulcan and the Anthropogenic Carbon Emissions System (ACES). As a step toward developing improved, accurate, and detailed FFCO2 emission landscapes, we perform a comparison of the two data products. We find that while agreeing on total FFCO2 emissions at the aggregate scale (relative difference = 1.7%), larger differences occur at smaller spatial scales and in individual sectors. Differences in the smaller-emitting sectors are likely errors in ACES input data or emission factors. ACES advances the approach for estimating emissions in the gas and oil sector, while Vulcan shows better geocoordinate correction in the electricity production sector. Differences in the subcounty residential and commercial building sectors are driven by different spatial proxies and ",27.0, b68a148d7e823bf92544f909e5948e7653a65a33,10.3390/land11091449,"Mapping Energy Poverty: How Much Impact Do Socioeconomic, Urban and Climatic Variables Have at a Territorial Scale?",2022.0,Land,Felipe Encinas; Ricardo Truffello; Carlos Aguirre-Nuñez; Isidro Puig; Francisco Vergara-Perucich; Carmen Freed; B. Rodríguez,"Energy poverty, considered a form of deprivation distinct from income poverty, is associated with three factors: low-income levels, high energy costs, and poor residential energy efficiency. It is necessary to study the socio-spatial distribution of energy poverty, particularly in metropolitan areas, due to persistent socioeconomic segregation and their public agenda implications, including the U.N. SDGs. A model of these characteristics can propose a spatial analysis of urban and climate implications, contributing evidence for public policy. This article aims to address energy poverty from a spatial approach extended to the urban area in Santiago de Chile through an exploratory model that estimates the impact of socioeconomic, urban, and climatic variables at a territorial scale on the performance of homes. Using a geographical weighted regression with the inside home temperature in winter as the dependent variable, the independent variables were the percentage of professionals, NDVI, annual thermal amplitude, and housing material quality. A housing quality pattern that acts as a proxy for vulnerability to energy poverty was found, repeating the distribution pattern of the differe",27.0,https://www.mdpi.com/2073-445X/11/9/1449/pdf?version=1662037108 c30509d1b7dff13ef9c6d68570be3618b239e0cd,10.3390/buildings11120592,An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand,2021.0,Buildings,F. Tajani; F. Di Liddo; M. Guarini; Rossana Ranieri; D. Anelli,"Due to the forced changes that the COVID-19 pandemic has had in many aspects of ordinary life (working, social life, training and learning in schools etc.), the characteristics of domestic spaces have significantly modified buying, selling and renting decisions in the real estate market. The aim of this research was to structure a methodology, articulated into four steps, to assess the variation that has occurred in residential market demand as a result of COVID-19 anti-contagion measures, with regard to six metropolitan Italian cities. We considered two samples of properties for each city, the first referencing the pre-COVID-19 pandemic spread and the second collected in the current situation. Consequently, we were able to determine variations in residential market appreciations through the application of a regressive econometric technique. The obtained results highlighted the relevance assumed by indoor acoustic and thermal comfort property factors, compared with the pre-COVID-19 condition. The proposed methodology could be useful to support the public and private entities involved in urban investment decision-making processes, allowing us to identify the most appreciated factors",27.0,https://www.mdpi.com/2075-5309/11/12/592/pdf?version=1638353822 4a61515c15442a7cab1691da3c05615351b1ae6e,10.1177/23998083231153401,"Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA",2023.0,Environment and Planning B Urban Analytics and City Science,A. Lotfata; George Grekousis; Ruo-Yu Wang,"In the United States, the rise in hypertension prevalence has been connected to neighborhood characteristics. While various studies have found a link between neighborhood and health, they do not evaluate the relative dependence of each component in the growth of hypertension and, more significantly, how this value differs geographically (i.e., across different neighborhoods). This study ranks the contribution of ten socioeconomic neighborhood factors to hypertension prevalence in Chicago, Illinois, using multiple global and local machine learning models at the census tract level. First, we use Geographical Random Forest, a recently proposed non-linear machine learning regression method, to assess each predictive factor’s spatial variation and contribution to hypertension prevalence. Then we compare GRF performance to Geographically Weighted Regression (local model), Random Forest (global model), and OLS (global model). The results indicate that GRF outperforms all models and that the importance of variables varies by census tract. Household composition is the most important factor in the Chicago tracts, while on the other hand, Housing type and Transportation is the least important",27.0, a66a5db8fbe78631397695c1854f9f551e8b4539,10.1109/jstars.2024.3416184,Drivers of Land Surface Temperatures From the Perspective of Urban Functional Zones,2024.0,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,Yi Bai; Liang Liu; Jun Yang; Junjie Wang; Qiyue Zou,"Urban areas have a high level of spatial heterogeneity. Therefore, examining the degree of drivers in urban functional zones (UFZs) is important. In this study, point-of-interest data were used primarily to identify UFZs. Random forest and geographically weighted regression models were used to analyze the influence of drivers on land surface temperature (LST). Landsat imagery showed that the urban center had a significantly higher LST than the coast and forested mountains in downtown Dalian. Residential zones accounted for most of the uptown area (37%). UFZs in built-up areas generally had high-temperature levels, exceeding 37 °C. The normalized difference vegetation index was the most important in almost all UFZ types. Modified normalized difference water index and albedo showed uncertain positive/negative correlations in different UFZs. There was also a positive correlation between UFZs and road density. The importance and correlation between the gross domestic product and population density were not statistically significant. The patch density (PD) was similar to the landscape shape index in terms of importance; however, the PD was relatively suitable in terms of correlation. Th",27.0,https://doi.org/10.1109/jstars.2024.3416184 2e1252913e8839541fe63e7c09b2b654a8b98c5a,10.1016/j.scitotenv.2021.145237,Modeling elevated blood lead level risk across the United States.,2021.0,Science of the Total Environment,D. Wheeler; Joseph R. Boyle; Shyam Raman; Erik J. Nelson,"Lead exposure adversely affects child health and continues to be a major public health concern in the United States (US). Lead exposure risk has been linked with older housing and households in poverty, but more studies of neighborhood socioeconomic status (SES) and lead exposure risk over large and diverse geographic areas are needed. In this paper, we combined lead test result data over many states for a majority of the US ZIP Codes in order to estimate its association with many SES variables and predict lead exposure risk in all populated ZIP Codes in the US. The methods used for estimation and prediction of lead risk included the Vox lead exposure risk score, random forest, weighted quantile sum (WQS) regression, and a Bayesian SES index model. The results showed that the Bayesian index model had the best overall performance for modeling elevated blood lead level (EBLL) risk and therefore was used to create a lead exposure risk score for US ZIP Codes. There was a statistically significant association between EBLL risk and the SES index and the most important SES variables for explaining EBLL risk were percentage of houses built before 1940 and median home value. When mapping th",27.0, d6587e5fec1f171df1141e2a3aa2bbb6d1d89339,10.1016/j.irle.2007.12.002,Who wants safer cities? Perceptions of public safety and attitudes to migrants among China's urban population,2008.0,,I. Nielsen; Russell Smyth,"Most extant research in the economics of crime literature has focused on explaining variations in crime rates. Public action to prevent crime, however, is often dependent on the level of concern about public safety that is expressed in public perceptions surveys. The economics of crime literature has largely overlooked responses to such surveys as data sources and therefore it has not accounted for the role that public opinion might have in mobilizing public action against crime. We use a unique survey administered in 2003 in 32 Chinese cities to examine the determinants of perceptions of public safety among China's urban population. One of our major findings is that individuals who have a negative perception of rural-urban migrants living in their city have a poor perception of public safety. We also find that the unemployment rate, the masculinity ratio and expenditure on armed police in the city in which the individual resides, whether the individual lives in the coastal region as opposed to the central or western region and average changes in housing prices and average changes in rental prices in the city in which the individual lives are important predictors of perceptions of ",27.0,https://figshare.com/articles/journal_contribution/Who_wants_safer_cities_Perceptions_of_public_safety_and_attitudes_to_migrants_among_China_s_urban_population/20919583 5715f2a728adab70aeec6dc7c0f56cf7372fe33a,10.1109/access.2021.3077962,A Machine Learning-Based Early Warning System for the Housing and Stock Markets,2021.0,IEEE Access,Daehyeon Park; Doojin Ryu,"This study analyzes the relationship between the housing and stock markets, focusing on housing market bubbles. Stock market dynamics generally have a more significant impact on housing price movements than housing market dynamics have on stock dynamics. However, if housing market information is provided as a signal, housing price movements can predict stock market volatility. Accordingly, we build a machine learning-based early warning system (EWS) for the housing market using a long short-term memory (LSTM) neural network. Applying the generalized supremum augmented Dickey-Fuller test to extract the bubble signal in the housing market, we find that the signal simultaneously detects future changes in the housing market prices and future stock market volatility, and our EWS effectively detects the bubble signal. We confirm that the LSTM approach performs better than other benchmark models, the random forest and support vector machine models.",27.0,https://doi.org/10.1109/access.2021.3077962 97b5301021c6ee5d8bfe5415d2ac298dbd9c4f2c,10.1177/0042098015608090,Interurban house price gradient: Effect of urban hierarchy distance on house prices,2016.0,Architecture and the Built Environment,Yun-Long Gong; P. Boelhouwer; J. de Haan,"This paper applies a general spatial equilibrium model to investigate the effect that distance within the urban hierarchy can have on interurban house prices. Our spatial model predicts a negative price gradient towards higher-tier cities, which can be decomposed into a ‘productivity component’ and an ‘amenity component’, representing respectively the effect of wage differences and households’ valuation of access to higher-order services. The theoretical findings are tested on data for the hierarchical urban system of the Pan-Yangtze River Delta in China. Both central and subcentral cities are shown to impose statistically significant distance penalties on interurban house prices, even after we control for amenities and characteristics that are generally considered to be the determinants of house prices. According to the empirical decomposition, the negative house price gradients are largely accounted for by the productivity component.",27.0,http://resolver.tudelft.nl/uuid:ba0f6389-10c5-4fe6-bfe5-186b4086b372 0386c844546c5222f090ddb5bc12044055d8a703,10.3390/su11030938,The Effect of Land Use on Housing Price and Rent: Empirical Evidence of Job Accessibility and Mixed Land Use,2019.0,Sustainability,Danya Kim; Jangik Jin,"Recently, the improvement of job accessibility and the encouragement of mixed land use have been gaining popularity in the planning field. However, little is known about whether these two factors are able to meet housing consumers’ needs. This study aims to analyze how job accessibility and mixed land use satisfy housing consumers’ needs. Particularly, this study investigates housing consumers’ willingness to pay for these two features by using housing prices and rents in the Chicago metropolitan area. In order to deal with endogeneity between land use and housing prices and spatial autocorrelation between housing prices, spatial econometric models are used with instrumental variables. Interestingly, our findings show that an increase in job accessibility leads to an increase in housing prices, whereas it is not related to rents. We also found that mixed land use decreases housing prices, but increases rents.",27.0,https://www.mdpi.com/2071-1050/11/3/938/pdf?version=1549979194 318cd92a82d370ab9585b828ea7e74f7b3f153cd,10.1257/jep.37.2.29,"Neighborhood Change, Gentrification, and the Urbanization of College Graduates",2023.0,Journal of Economic Perspectives,Victor Couture; Jessie Handbury,"We study changing trends in within-city sorting by education over the last 40 years. We show that neighborhoods closest to the centers of large US cities rose from having the lowest levels of college attainment in 1980 to the highest in 2017. We discuss the determinants of changes in sorting patterns, focusing on the role of transportation technology and income growth. We outline various consequences of the recent urbanization of college graduates on neighborhood amenities, house prices, and segregation. We highlight the tendency of college graduates to cluster into select central neighborhoods, likely limiting opportunities for interactions across educational lines.",27.0,https://www.aeaweb.org/articles/pdf/doi/10.1257/jep.37.2.29 35bc8db1fa0be4775efa534ceb37ddf6b039e25f,10.35940/ijrte.b1084.0982s1119,"Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia",2019.0,International journal of recent technology and engineering,,"This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.",27.0,https://doi.org/10.35940/ijrte.b1084.0982s1119 7ce4466d0577f2e1eb6778fa897e382453cb2d63,10.1111/1540-6229.12246,The Timeline Estimation of Bubbles: The Case of Real Estate,2018.0,Real Estate Economics,F. Fabozzi; Ke-Li Xiao,"We develop and then apply a new recursive regression methodology with a two‐direction searching process for initialization. The methodology improves the reliability of existing models when estimating a bubble's timeline. We apply our proposed methodology to estimate bubbles in U.S. home prices as well as in simulated scenarios. Our results confirm the improvement in reliability of the proposed methodology in obtaining consistent estimators with varying samples. Moreover, we verify the presence of bubbles in the U.S. aggregate data and seven of the eight cities in our study prior to the subprime crisis and find evidence of the bubble's reemergence since October 2013.",27.0, ac1e72e7c21e4027ff459cab555e207a319cea0b,10.1007/s00168-020-01040-z,Determinants of house prices in China: a panel-corrected regression approach,2021.0,,Mei Liu; Qing Ma,,27.0, 516bcc9addca455f8a1568c1056ae294bbc3bb69,10.1016/j.ecotra.2022.100272,Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach,2022.0,Economics of Transportation,Guojian Zou; Ziliang Lai; Ye Li; Xing-Hua Liu; Wenxiang Li,,27.0, ccde446e06e8cf48e16b4088c2f68701ad42f921,10.63642/1357-1419.1047,Performance comparison of automated valuation models,2006.0,Journal of Property Tax Assessment & Administration,J. W. Moore,"Because it is necessary for assessors to prepare value estimates for huge numbers of properties, all at a specific point in time each year, a process called computer assisted mass appraisal (CAMA), which uses Automated Valuation Models (AVMs), has evolved during the past 35 years to handle the logistic challenge presented by the task. Several CAMA methodologies using different AVMs exist for determining the assessed value of residential properties. A large research study and controlled experiment that statistically tested the results of the several CAMA methodologies when applied under the same circumstances has not been reported until now. This research used all valid market sale transactions for the five years of 1999 through 2003 to estimate selling prices of an actual residential property population in an assessing jurisdiction by using four different CAMA methodologies. The model specification, calibration and value estimation work was done blindly by nine independent CAMA practitioners from across North America without knowledge of the source of the data, the actual 2004 sale prices, or even names of other participants. Six of the participants used a generic AVM market model ",26.0, 352dd20bc0e01f0e4928aed230bf1afde229f2df,10.1016/j.msard.2022.103994,The impact of socioeconomic status on subsequent neurological outcomes in multiple sclerosis,2022.0,Multiple Sclerosis and Related Disorders,Devi Sai Sri Kavya Boorgu; Shruthi Venkatesh; C. Lakhani; Elizabeth Walker; Ines M Aguerre; Claire S. Riley; C. Patel; P. De Jager; Zongqi Xia,"Background: To examine whether lower neighborhood-level and individual-level indicators of socioeconomic status (SES) are associated with subsequently worse neurological disability in people with MS (pwMS). Methods: In a multi-center study using prospectively collected data from discovery cohorts (University of Pittsburgh, N=1316) and replication cohorts (Columbia University, N=488), we calculated a neighborhood SES indicator, area deprivation index (ADI), based on participants’ residence at enrollment, and we derived an individual SES indicator based on participants’ household income. Patient-reported neurological outcomes included the Multiple Sclerosis Rating Scale–Revised (MSRS-R), Patient-Determined Disease Steps (PDDS), and Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function scores from 2018 to 2020. We performed covariate-adjusted regression analyses in each cohort and then random-effects meta-analyses. Results: Higher ADI (lower SES) in 2015 was associated with subsequently worse neurological outcomes during 2018–2020 (discovery: MSRS-R, β=0.62, 95%CI [0.36,0.89], p<0.001; PDDS, β=0.11, 95%CI [0.02,0.20], p=0.02 | replication: MSRS-R, β=0.46,",26.0,https://doi.org/10.1016/j.msard.2022.103994 5b216cc4fd0bc8be36047e58ad0c63ef39b4a9c5,10.1016/j.socscimed.2023.115963,Social and physical neighbourhood characteristics and 10-year incidence of depression and anxiety in older adults: Results from the Longitudinal Aging Study Amsterdam.,2023.0,Social Science & Medicine (1967),I. Motoc; E. Hoogendijk; E. Timmermans; D. Deeg; B. Penninx; M. Huisman,"OBJECTIVE A growing literature suggests that neighbourhood characteristics are associated with mental health outcomes, but the evidence in older adults is inconsistent. We investigated the association of neighbourhood characteristics, pertaining to demographic, socio-economic, social and physical environment domains, with the subsequent 10-year incidence of depression and anxiety, in Dutch older adults. METHODS In the Longitudinal Aging Study Amsterdam depressive and anxiety symptoms were assessed four times between 2005/2006 and 2015/2016, using the Center for Epidemiological Studies Depression Scale (n = 1365) and the Anxiety subscale of the Hospital Anxiety and Depression Scale (n = 1420). Neighbourhood-level data on urban density, percent population over 65 years of age, percent immigrants, average house price, average income, percent low-income earners, social security beneficiaries, social cohesion, safety, proximity to retail facilities, housing quality, percent green space, percent water coverage, air pollution (particulate matter (PM2.5)), and traffic noise, were obtained for study baseline years 2005/2006. Cox proportional hazard regression models, clustered within neig",26.0,https://doi.org/10.1016/j.socscimed.2023.115963 65c900177d2d2fe8ab1922b74fdba1e36d5fa657,10.3390/land10101103,Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big Data,2021.0,Land,Hanbing Yang; Mei-Chen Fu; Li Wang; Feng Tang,"The tense relationship between the supply and demand of land resources and the past spatial expansion of urban development in Beijing have brought many urban problems. Mixed land use is considered to be able to solve these urban problems as well as promote sustainable urban development. In this context, this study uses multi-source big data such as POI, OpenStreetMap and web crawler data to construct current land-use data of the area within the sixth ring road of Beijing, and then uses the entropy index and type number index to analyze the spatial distribution and aggregation characteristics of the mixed land-use level. Finally, a multi-scale geographically weighted regression is applied to explore the impact of the block and life circle scale mixed land use on housing prices. The results show that: (1) the accuracy of land use data obtained by using multi-source big data is high, and the consistency with the real land use situation is as high as 82.67%. (2) the mixed land use level in the study area is higher in the urban center and lower in the periphery of the city. However, it does not show the spatial distribution characteristics gradually decreasing with the increase of the d",26.0,https://www.mdpi.com/2073-445X/10/10/1103/pdf?version=1634721785 df0846665240021ce0cb10db1c25be7d053276a9,10.1007/s11676-022-01535-9,Understanding the drivers of woody plant diversity in urban parks in a snow climate city of China,2022.0,Journal of Forest Research,Zijun Ma; P. Zhang; Nanlin Hu; Guodong Wang; Yulin Dong; Yujie Guo; Cheng-Cong Wang; Yao Fu; Z. Ren,"Urban parks are an important part of urban ecosystems and play an important role in biodiversity conservation. However, it is still unclear how park characteristics affect plant diversity which could hinder the conservation of urban biodiversity due to the ineffective design of urban parks. The diversity of woody plants in 33 parks of Changchun, China, was measured with plot measurements and linked with urban park characteristics (e.g., size and age of the park) to uncover the relationship between them. The results show that urban woody plant species were abundant, with 98 species belonging to 51 genera and 26 families in the snow climate city of Changchun. The variation in woody plant diversity was largely explained by internal patch characteristics (e.g., size, age, shape), and external factors surrounding the park (e.g., land use type and socioeconomic level) accounted for only 16.0% in our study. For internal patch characteristics, older urban parks with larger areas demonstrated a richer level of plant diversity and increased nonlinearly with increasing park area. The threshold size significantly affecting plant diversity variation was approximately 30 ha. Plant diversity had ",26.0,https://link.springer.com/content/pdf/10.1007/s11676-022-01535-9.pdf fe4ab4ad0f9936696709901df7b662e7ee0e156f,10.1007/s10901-024-10134-5,Determinants of housing prices: Serbian Cities’ perspective,2024.0,Journal of Housing and the Built Environment,S. Marinković; Marija Džunić; I. Marjanović,"This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the in",26.0,https://doi.org/10.1007/s10901-024-10134-5 c594a42b1268a4e85ddccbe2b0dfc68f5c36f1b8,10.1007/s10614-022-10298-8,A Novel Hybrid House Price Prediction Model,2020.0,Computational Economics,Süreyya Özöğür Akyüz; Birsen Eygi Erdogan; Ö. Yıldız; Pınar Karadayı Ataş,"The real estate sector is evolving and changing rapidly with the increase in housing demand, and new luxury housing projects appear every day. The reliability of housing market investments is largely dependent on accurate pricing.The aim of this study is to introduce a dynamic pricing procedure that estimates house prices using the most important characteristics of a house. For this purpose, a hybrid algorithm using linear regression, clustering analysis, nearest neighbor classification and Support Vector Regression (SVR) method is proposed. Our hybrid algorithm involves using the output of one method as the input of another method for home price prediction to deal with the heteroscedastic nature of the housing data. In other words, the aim of this study is to present a hybrid algorithm that will create different housing clusters from the available data set, classify the houses to which the cluster is unknown, and make price predictions by creating separate prediction models for each class. Housing data collected through manual web scraping of Kadıköy district in Istanbul were used for training and validation of the proposed algorithm. In addition to these data, we validated our al",26.0,https://www.authorea.com/doi/pdf/10.22541/au.160284063.37909495 29c12f3bb0854d6a1ce4511dd8458406395a473a,10.1016/j.compenvurbsys.2017.04.005,Utilizing a cellular automaton model to explore the influence of coastal flood adaptation strategies on Helsinki's urbanization patterns,2017.0,"Computers, Environment and Urban Systems",Athanasios Votsis,"Abstract A cellular automaton model (SLEUTH-3r) is utilized to explore the impacts of coastal flood risk management strategies on the urbanization parameters of Helsinki's metropolitan area, at a 50-m spatial resolution by 2040. The current urbanization trend is characterized by the consolidation of existing built-up land and loss of interspersed green spaces, whereas the most intense growth is forecast inside the coastal flood risk areas. This baseline is compared to strategies that test various responses of the planning system to real estate market forces and the spatial distribution of flood risks. A set of scenarios translates property price effects of flood risk information into various attraction-repulsion areas in and adjacent to the floodplain, while a second set explores varying degrees of restricting new growth in the flood risk zones without reference to the housing market. The simulations indicate that growth under all scenarios is distributed in a more fragmented manner relative to the baseline, which can be interpreted favorably regarding house prices and increased access to ecosystem services, although the indirect effects should also be considered. Demand for coasta",26.0,https://www.sciencedirect.com/science/article/pii/S0198971517301874/pdf 592abbde929973d791ea267051e6a0a5c507a339,10.1080/20964471.2022.2031543,Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model,2022.0,Big Earth Data,N. Liu; J. Strobl,"ABSTRACT The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai, as well as structure and location characteristics. Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018. Six neighborhood features, structural and location characteristics, are selected according to their statistical significance and multicollinearity test results from an OLS model. Regression analysis is performed by OLS, GWR, and MGWR to compare their performance in housing price research at community level. The comparison of the three models also demonstrates that the GWR (66%) and MGWR (68%) models perform much better than OLS model (52%). MGWR is not significantly different from GWR in areas with few sample points, and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area. The regression parameter indicates that building age is the most important factor among all influencing factors. Proximity to schools and factories have positive and negative significant effects on housing resale prices, respectively. The spatial pattern of neighborho",26.0,https://doi.org/10.1080/20964471.2022.2031543 ef3aca63f7b17683d98f83c89c4c58513c8d7d31,10.17576/jem-2020-5401-11,Macroeconomic Determinants of House Prices in Malaysia,2020.0,Jurnal Ekonomi Malaysia,Saizal Pinjaman; M. Kogid,"House prices in Malaysia are considered to be seriously unaffordable as the median all-house price is relatively higher than the annual median income. Although the issue of house prices is prevalent in the country, few studies have been done to determine factors that influence its movement. The current paper, therefore, attempts to investigate the causal relationship between macroeconomic variables and house prices in Malaysia by accounting for the existence of a structural break for the variables. It is identified that in the long run, macroeconomic variables are collectively significant in influencing house price movement while the individual impact of macroeconomic variables is varied. The rise in the level of interest rates, housing supply, and inflation will result in the decline in house prices while gross domestic product and local currency appreciation cause the price to increase. It was found that stock prices do not significantly influence house prices. Of all the macroeconomic factors analyzed, exchange rate fluctuations appear to be most significant in explaining the movement of house prices. In the short-run, all macroeconomic factors are individually significant in in",26.0,https://eprints.ums.edu.my/id/eprint/26526/2/Macroeconomic%20Determinants%20of%20House%20Prices%20in%20Malaysia1.pdf 7e6fb62a048556918dfc347689326e1eec45eea6,10.3390/su14063485,Space Syntax in Analysing Bicycle Commuting Routes in Inner Metropolitan Adelaide,2022.0,Sustainability,A. Soltani; A. Allan; M. Javadpoor; Jaswanth Lella,"Cycling is a particularly favoured for short urban trips because it is a healthy and environmentally benign activity. As a result, urban mobility, quality of life, and public health are enhanced, while traffic congestion and pollution are decreased. In looking beyond the street network in terms of how it affects cyclists’ behavior choices, Bill Hillier’s (1984) outstanding legacy research on spatial space syntax is investigated in this study. The goal of this study is to determine if an urban area’s street network morphology influences commuters’ inclination to ride their bicycles to work. To further understand the nonlinear consequences of street network geometry on the estimation of cycling to work, a logarithmic-transformed regression model that includes base socioeconomic components, urban form, and street network variables represented by space syntax measure factors is developed. In conclusion, this model determined that bike commuting choice is significantly associated with the centrality index of Connectivity, although this is in combination with socioeconomic factors (age, gender, affluence, housing type, and housing price) and built environment factors (share of commercial",26.0,https://www.mdpi.com/2071-1050/14/6/3485/pdf?version=1647850013 aa82db73a0ef7b24a4266aa846327f66030956a0,10.1080/09599916.2019.1697345,A spatial analysis of EPCs in The Belfast Metropolitan Area housing market,2019.0,Journal of Property Research,M. McCord; D. Lo; P. Davis; Lesley Hemphill; John McCord; M. Haran,"ABSTRACT Energy performance remains a debated topic in real estate, particularly with reference to the capitalisation effect with property value. An emerging corpus of research studies have investigated the relationship between energy performance characteristics and the role of Energy Performance Certificates. Whilst these studies have consistently demonstrated that a pricing effect exists, some recent studies have shown that Energy Performance Certificates (EPCs) are more complex and inconclusive, particularly when accounting for data limitations and changing model specifications. Moreover, a majority of these studies neglect to adequately account for absolute location and therefore, arguably, do not examine the geographic variation between EPCs and property value across the housing market setting. This study presents one of the first spatial analyses of EPCs using transactions for the Belfast Metropolitan Area. In evaluating whether spatial effects exist between EPCs and house prices, a number of spatial tests are performed and a series of models are developed to account for spatial dependency and determine whether there are any spatially correlating effects. The findings indicat",26.0,https://pure.ulster.ac.uk/ws/files/77773109/JPR_EPC_accepted_manuscript.pdf 4655346882b2c7a753703819b2b182fc3a66cc02,10.3390/su10041026,Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework,2018.0,,Ya-Xiong Ma; S. Gopal,"Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR) and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC) values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use ",26.0,https://doi.org/10.3390/su10041026 2590f2b5251dbc8d9f3c01df87e0e3930d6e21af,10.30534/ijeter/2019/247112019,Improving Accuracy by applying Z-Score Normalization in Linear Regression and Polynomial Regression Model for Real Estate Data,2019.0,International Journal of Emerging Trends in Engineering Research,D. Anggoro,"This research aimed to prove whether a normalization concept, i.e., z-score normalization was able to improve the accuracy result of algorithms for predicting house prices in Sindian District, New Taipei City, Taiwan. There were specific techniques in data science, e.g., linear regression and polynomial regression, that became the focus of this research. This study used several features that were implemented to model the house price. The features used included the transaction date, the house age, the distance to the nearest MRT, the number of nearby convenience stores, the latitude geographic coordinate, and the longitude geographic coordinate. The data were then preprocessed by splitting it into a training dataset (75%) and a testing dataset (25%) using a simple random sampling method. The subsequent step was to process the dataset with linear regression and polynomial regression model. Based on the result of the data processing, the optimum order befell to quadratic polynomial regression – maximum order was 2. This algorithm was then applied to normalized datasets and earned low scores of Mean Squared Error (MSE) and R-squared score, which were 7.044 x 10 -7 and 0.989, respective",26.0,https://doi.org/10.30534/ijeter/2019/247112019 e0c212843feb4645d9193de890ad2c245a8b7cd2,10.1007/s11116-021-10248-7,Bus ridership and its determinants in Beijing: A spatial econometric perspective,2022.0,Transportation,Jiaoe Wang; Ya-Nan Li; Jing-Juan Jiao; Hai-Tao Jin; Fang-Ye Du,"Understanding the temporal and spatial dynamics and determinants of public transport ridership play an important role in urban planning. Previous studies have focused on exploring the determinants at the station level using global models, or a local model, geographically weighted regression (GWR), which cannot reveal spatial autocorrelation at the global level. This study explores the factors affecting bus ridership considering spatial autocorrelation using the spatial Durbin model (SDM). Taking the community in Beijing as the basic study unit, this study aims to explore the temporal and spatial dynamics of bus ridership and identify its key determinants considering neighboring effects. The results show the following: (1) The temporal dynamics are quite distinct on weekdays and weekends as well as at different time slots of the day. (2) The spatial patterns of bus ridership varied across different time slots, and the hot areas are mainly located near the central business district (CBD), transport hubs, and residential areas. (3) Key determinants of bus ridership varied across weekends and weekdays and varied at different time slots per day. (4) The spatial neighboring effects had b",26.0,https://link.springer.com/content/pdf/10.1007/s11116-021-10248-7.pdf eef9149bae91aacabd912a06993f137e37d49971,10.2139/ssrn.1616616,Relative House Price Dynamics Across Euro Area and US Cities: Convergence or Divergence?,2010.0,Social Science Research Network,P. Hiebert; M. Roma,"This paper examines the time varying dispersion in city house price levels across the four biggest euro area countries compared with those in the United States. Using available city-level data over the period 1987-2008, it tests for price convergence and analyses key factors explaining price differentials in a panel regression framework including per capita income, population and relative distances. Results indicate limited evidence of convergence in city-level house prices despite synchronised cycles in the national aggregates for most countries since the 1990s. There is an important role for income differentials in explaining city-level house price dispersion in Germany, France, and the US (but not in Italy or Spain once unobserved city factors are taken into account). At the same time, population differences across cities play a role, though this appears to be associated with amenities specific to a particular location. In general, there has been a lower dispersion of city-level house prices in the four largest euro area economies compared with the US in conjunction with a lower estimated income elasticity for house price differentials. The results, particularly for income, appe",26.0,https://doi.org/10.2139/ssrn.1616616 3e81eba6facfb7c974f2e4517dd5f734d18ee70b,10.32479/ijefi.10262,FACTORS DETERMINING HOUSING DEMAND IN SAUDI ARABIA,2020.0,,Hussain Mohammed A. Al Obaid,"Against the backdrop of real estate development and rising prices, which affect the affordability of housing in Saudi Arabia, this study empirically estimates housing demand using time-series analysis from 1987 to 2016. To find the main factors affecting housing demand in Saudi Arabia, this study developed an econometric model with explanatory variables such as income level, housing prices, population growth rate, demand for loans, and consumer price index (CPI). The results show that demand for housing is inelastic with respect to price, income, and population growth rate; moreover, the price elasticity of demand is less than that of income and population growth rate, in absolute terms. A regression analysis suggests that the most significant factors that determine housing demand in Saudi Arabia are population growth rate, CPI and demand for housing loans. This study was conducted with a particular focus on the Saudi Arabian market and showed some surprising results, which are not consistent with popular economic theories.",26.0,https://doi.org/10.32479/ijefi.10262 24c2b1f1f87874956c685a0f04e2e25306283265,10.2139/ssrn.2514729,House Price Expectations,2014.0,Social Science Research Network,G. Niu; A. van Soest,"Utilizing new survey data collected between 2009 and 2014, this paper analyzes American households' subjective expectations on future home values. We explore the relationship between house price expectations, local economic conditions, and households' individual characteristics. We examine the heterogeneity in expectations based on panel data models. In particular, we estimate the individual- and time-specific subjective probability distributions for five-year-ahead home values. House price expectations vary significantly over time, and are positively related to past housing returns and perceived economic conditions. There is large variation in both the central tendency and the uncertainty of expectations on future home values across individuals, which is associated with several socio-economic and demographic factors. Comparing expectations and realizations shows that households only partially anticipated the large downward changes in home values in the time period 2009-2011.",26.0,https://doi.org/10.2139/ssrn.2514729 fc2bdcb97cee51f67406df03ddf41763e8d54f52,10.1111/saje.12246,Measuring the Financial Cycle in South Africa,2020.0,South African Journal of Economics,G. Farrell; Esti Kemp,"We measure the financial cycle of South Africa using three different methodologies. The financial cycle is identified using credit, house prices and equity prices as indicators, and estimated using traditional turning‐point analysis, frequency‐based filters and an unobserved components model‐based approach. We then consider the financial cycle’s main characteristics and examine its relationships with the business cycle. We confirm the presence of a financial cycle in South Africa that has a longer duration and a larger amplitude than the traditional business cycle. Developments in measures of credit and house prices are important indicators of the financial cycle, although the case for including equity prices in the measures is less certain. Periods where financial conditions are stressed are associated with peaks in the financial cycle, suggesting that the estimated financial cycle may have similar leading indicator properties to financial conditions or stress indices.",26.0,https://doi.org/10.2139/ssrn.3079369 7069a700f5fc0bfe776b0193be0f66b8aeeed0e7,10.3846/1648-715x.2009.13.287-298,Study of guangzhou house price bubble based on state‐space model,2009.0,,E. Hui; Qin-Hua Gu,"Previous literature has explored Guangzhou's real estate market bubble problem, by using the perspective of rent receipts to measure the fundamental housing value. Since there is high proportion of owner‐occupied property in Guangzhou; and that the household income is considered a key factor affecting housing price level, this article is from the perspective of household income, by the present asset market model, it sets up housing price model that analysis of the bubble phenomenon, and uses the state‐space model that can be estimated characteristics by unobservable variables. The article estimates the price bubble and analyzes the size of the bubble at different times. The housing price in Guangzhou from January 2004 to December 2008 is studied. The results concluded that the housing market bubble peaked in October 2007 at around 43% of the market housing price.",26.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/6197/5387 7fae69b5a36e018a1059990deb5a38555a6a59f1,10.2478/remav-2019-0031,The Impact of Macro Factors on Apartment Prices in Polish Counties: A Two-Stage Quantile Spatial Regression Approach,2019.0,,Mateusz Tomal,"Abstract The aim of this study is to identify the economic, demographic, environmental and spatial determinants of residential property prices in Polish counties. Two-stage quantile spatial regression (2SQSR) is the main method of analysis. Additionally, two-stage least squares regression (2SLS) is used. The estimation results indicate that there are several significant determinants of residential property prices with observable quantile effects. In particular, the findings reveal a very interesting U-shaped pattern among quantile regression coefficients for variables describing unemployment rate, the standard of properties and county area. For the spatially lagged dependent variable, a linear pattern is observable. Generally, the findings of this study have notable implications for developing housing policies, both at the national and local levels.",26.0,https://doi.org/10.2478/remav-2019-0031 f97cf45a98417fdf2f180c8611b197ac9bdfbebb,10.1111/1540-6229.12326,Appraising home purchase appraisals,2018.0,Real Estate Economics,P. Calem; Lauren Lambie-Hanson; L. Nakamura; Jeanna Kenney,"Home appraisals are produced for millions of residential mortgage transactions each year, but appraised values are rarely below the purchase contract price: Some 30% of appraisals in our sample are exactly at the home price (with less than 10% of them below it). We lay out a basic theoretical framework to explain how appraisers? incentives within the institutional framework that governs mortgage lending lead to information loss in appraisals (that is, appraisals set equal to the contract price). Consistent with the theory, we observe a higher frequency of appraisal equal to contract price and a higher incidence of mortgage default at loan-to-value boundaries (notches) above which mortgage insurance rates increase. Appraisals appear to be less informative for default risk measurement compared with automated valuation models.",26.0,https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2017/wp17-23.pdf 686ed6a4fc05e40b3d14f53a09c4e56db995d402,10.1016/j.jhe.2018.01.001,When warm and cold don’t mix: The implications of climate for the determinants of homelessness,2018.0,Journal of Housing Economics,Kevin Corinth; D. Lucas,"It is widely understood that climate affects the spatial distribution of homelessness—warm places have on average higher rates of unsheltered homelessness than cold places. A less recognized fact is that variation in rates of unsheltered homelessness is higher in warm places as well. We document this fact using quantile regression techniques and show that it has important implications for estimating the determinants of homelessness across communities. In particular, housing prices, poverty rates and religiosity are much more strongly associated with rates of unsheltered homelessness in warm places than in cold places. As an alternative to splitting the sample, we find that logarithmic transformations of rates of unsheltered homelessness can be reliably used in a pooled sample. Associations between total homelessness and important covariates also vary across warm and cold places, in this case in terms of both rates and logarithms. Ultimately, future research should carefully account for climate when estimating the determinants of homelessness.",26.0, 3c82c1a93b78ade1794352d0576c1e7dd76274f7,10.1016/j.jue.2019.04.001,Re-evaluating the impact of immigration on the U.S. rental housing market,2019.0,Journal of Urban Economics,James Sharpe,"Previous studies provide evidence that immigration increases housing prices and rents. To deal with endogenous location choices of immigrants, these studies often use a shift-share instrumental variable approach. This approach, however, fails to adequately account for the natural attraction of immigrants to cities with thriving economies. High-immigration cities provide more economic opportunities and thus exhibit persistently rising housing prices and rents. This paper improves upon the traditional empirical approach by explicitly controlling for initial city characteristics that lead to both increases in immigration and the evolution of rents. Results suggest that after controlling for endogenous sorting of immigrants, the positive effect of immigration on rents is attenuated. While the impact of immigrant inflows is smaller, an extension of the main results suggests the effects of immigration on rents remains larger than that of native inflows.",26.0, b097c0b1141b16aa8e9b71dcb6e78497b85c7140,10.1515/roe-2015-0106,Determinants of house price dynamics. What can we learn from search engine data?,2014.0,,Marco Oestmann; Lars Bennöhr,,26.0,https://doi.org/10.2139/ssrn.2513199 f23c142b0745d160d7217c8d265c27c6e2e3579e,10.1061/(asce)up.1943-5444.0000458,Capitalization of Urban Green Vegetation in a Housing Market with Poor Environmental Quality: Evidence from Beijing,2018.0,Journal of urban planning and development,Ying-Dan Mei; Xiao-Li Zhao; Lu Lin; Li Gao,AbstractUrban green vegetation provides amenity value for urban residents and improves the living environment. These changes in value can be reflected in the variation of house prices. This paper u...,26.0, 745746c9054e08a7daa0c52111e272463dbf91a8,10.1080/02723638.2015.1057397,The modifiable areal unit problem in hedonic house-price models,2016.0,,Gunhak Lee; Daeheon Cho; Kamyoung Kim,,26.0, 3eccc8181f1ca899ac3b2087dd76005869e5572e,10.1080/09599916.2017.1400575,House price determinants in Athens: a spatial econometric approach,2017.0,,Marianthi Stamou; A. Mimis; Antonis Rovolis,,26.0, 98fc3cf59851eba5b1923758e4fad0e45895d23e,10.1080/00036849200000085,Development of standardized indices for measuring house price inflation incorporating physical and locational characteristics,1992.0,,M. Fleming; J. Nellis,,26.0, cfe8085fbca43861bf25cc21701f181b40f00697,10.1016/j.ecolind.2021.107462,"Tree abundance, diversity and their driving and indicative factors in Beijing’s residential areas",2021.0,,Min Jiao; Haoran Xue; Jingli Yan; Zhong Zheng; Jia Wang; Cheng Zhao; Lu Zhang; Wei-Qi Zhou,"Abstract Urban trees provide a range of ecological services for urban dwellers. This is particularly true in residential areas where these services are known and valued. The abundance and diversity of trees determine and indicate the provision of ecological services by trees, while various characteristics of residential areas determine and indicate the abundance and diversity of trees within those areas. Exploring the abundance and diversity of trees in residential areas and their major driving and indicative factors is important for evaluating, predicting, and thus improving the ecological services provided by trees. Here, we selected 87 residential areas in Beijing through spatially stratified random sampling. Unlike the quadrat survey that is usually used, we conducted a detailed investigation of the abundance and diversity of trees in residential areas through a tree-by-tree survey and explored the major driving and indicative factors of the abundance and diversity of the trees. In the 87 sampled residential areas, 69 tree taxa belonging to 34 families were identified, and we found that the 10 most common species accounted for 55.9% of all trees. Trees in the residential areas ",25.0,https://www.sciencedirect.com/science/article/pii/S1470160X21001278/pdf ee411caa2e0f4496bc6b3b2d153a62349774ace2,10.1542/peds.2022-058674,Developmental Trajectories of Autism,2023.0,Pediatrics,Christine Fountain; Alix S. Winter; K. Cheslack-Postava; P. Bearman,"Objective The goal of this study was to describe the typical, longitudinal, developmental trajectories of communication and social functioning in individuals with autism spectrum disorder (ASD) from childhood through adulthood and to determine the correlates of these trajectories. Methods Children with ASD who were born in California from 1992 through 2016 and enrolled with the California Department of Developmental Services were identified. Subjects with <4 evaluations in the database were excluded, resulting in a sample of 71,285 individuals. Score sequences were constructed based on evaluative items for communication and social functioning. Typical trajectories were identified using group-based latent trajectory modeling, and logistic regression was used to determine the odds of classification into a social adolescent decline trajectory by individual-, family-, and zip code-level factors. Results Six typical patterns of communication functioning and seven typical patterns of social functioning were identified. Whereas the majority of autistic individuals exhibit improved communication functioning as they age, the majority of individuals exhibit steady social functioning. A small",25.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551845 745dd4bc122b207f0499a321700efc1da8eb7e2b,10.3390/ijgi11120624,Analysis of Urban Vitality in Nanjing Based on a Plot Boundary-Based Neural Network Weighted Regression Model,2022.0,ISPRS Int. J. Geo Inf.,Yi Yang; Hong Wang; Shuhong Qin; Xiuneng Li; Yun-Feng Zhu; Yicong Wang,"As a representative indicator for the level and sustainability of urban development, urban vitality has been widely used to assess the quality of urban development. However, urban vitality is too blurry to be accurately quantified and is often limited to a particular type of expression of vitality. Current regression models often fail to accurately express the spatial heterogeneity of vibrancy and drivers. Therefore, this paper took Nanjing as the study area and quantified the social, cultural, and economic vitality indicators based on mobile phone data, POI data, and night-light remote sensing data. We also mapped the spatial distribution of comprehensive urban vitality using an improved entropy method and analyzed the spatial heterogeneity of urban vitality and its influencing factors using a plot boundary-based neural network weighted regression (PBNNWR). The results show: (1) The comprehensive vitality in Nanjing is distributed in a “three-center” pattern with one large and two small centers; (2) PBNNWR can be used to investigate the local regression relationships among the driving factors and urban vitality, and the fitting accuracy (95.6%) of comprehensive vitality in weekday",25.0,https://www.mdpi.com/2220-9964/11/12/624/pdf?version=1671099032 486c332723dd6d35a2b3bea860a87a1c1310ddb6,10.17645/up.v6i3.4274,Variable Arrangements Between Residential and Productive Activities: Conceiving Mixed-Use for Urban Development in Brussels,2021.0,Urban Planning,M. Ryckewaert; J. Zaman; Sarah De Boeck,"Mixing productive economic activities with housing is a hot topic in academic and policy discourses on the redevelopment of large cities today. Mixed-use is proposed to reduce adverse effects of modernist planning such as single-use zoning, traffic congestion, and loss of quality in public space. Moreover, productive city discourses plead for the re-integration of industry and manufacturing in the urban tissue. Often, historical examples of successful mixed-use in urban areas serve as a guiding image, with vertical symbiosis appearing as the holy grail of the live-work mix-discourse. This article examines three recent live-work mix projects developed by a public real estate agency in Brussels. We investigate how different spatial layouts shape the links between productive, residential, and other land uses and how potential conflicts between residents and economic actors are mediated. We develop a theoretical framework based on earlier conceptualisations of mixed-use development to analyse the spatial and functional relationships within the projects. We situate them within the housing and productive city policies in Brussels. From this analysis, we conclude that mixed-use should be ",25.0,https://www.cogitatiopress.com/urbanplanning/article/download/4274/4274 b1044e499a56401ca0d88a3a6545aff0c3cdb75e,10.3390/su12031281,"Spatial-Temporal Variation in the Impacts of Urban Infrastructure on Housing Prices in Wuhan, China",2020.0,,Fan Liu; Min Min; Ke Zhao; Wei-Yan Hu,"This study aims to investigate the spatial and temporal dynamics of housing prices associated with the urban infrastructure in Wuhan, China. The relationship between urban infrastructure and housing prices during rapid urbanization has drawn popular concerns. This article takes 619 residential communities during the period 2010 to 2018 in Wuhan’s main urban area as research units, and uses the geographically and temporally weighted regression (GTWR) model to study the spatial-temporal differentiation in the effects of urban infrastructure on housing prices. The results show that: 1) From 2010 to 2018, housing prices in Wuhan’s main urban area were generally on the rise, but the increment speed has shown an obvious periodic characteristic, the spatial distribution of housing prices has shown an obvious core and periphery distribution and the peak value area shifted from Hankou to Wuchang. 2) The influential factors of housing prices have significant spatiotemporal non-stationarity, while the impact, direction and intensity of the influential factors varies in time and space. Spatially, the influence factors show different differentiation rules for spatial distribution, and the influ",25.0,https://www.mdpi.com/2071-1050/12/3/1281/pdf?version=1581351790 4da8ae512a9971c46fa1ec78295414a68f5a2097,10.15195/v6.a16,"Buying In: Positional Competition, Schools, Income Inequality, and Housing Consumption",2019.0,Sociological Science,Adam Goldstein; Orestes P. Hastings,"Social scientists have suggested that a key sociobehavioral consequence of rising inequality is intensifying market competition for advantageous positions in the opportunity structure, such as residences that afford access to high-quality public schools. We assess empirical implications of inequality-fueled positional competition theories (PCTs) by analyzing the relationships between metropolitan income inequality, households’ efforts to secure residential positions in desirable school districts, and housing consumption behavior. We assemble a unique data set, which contains longitudinal information on household finances, residences, and geographic locations from the Panel Study of Income Dynamics; information on the quality of the school attendance areas in which these households reside; and information about the local real estate market. We find that greater inequality is associated with steeper housing price premia for residences in desirable areas, more pronounced social class sorting on school quality when relocating, and greater salience of schools relative to other housing amenities in families’ housing expenditure functions. Families in high-inequality regions exhibit modes",25.0,https://sociologicalscience.com/download/vol-6/may/SocSci_v6_416to445.pdf bcc88aa6a74894b8030f7071e7fd929a9fa08668,10.1007/s10901-023-10098-y,"Urban versus rural disparities in amenity proximity and housing price: the case of integrated urban–rural city, Sejong, South Korea",2024.0,Journal of Housing and the Built Environment,Jiyeon Shin; Galen D. Newman; Y. Park,"Due to the growing importance of accessibility to urban amenities on quality of life and neighborhood satisfaction in South Korea, various efforts have been made to improve accessibility to urban amenities by establishing a minimum standard, an approach called Living Social Overhead Capital (Living SOC); for example, one such standards states that an elementary school should be reachable within a 15-min walk. However, these endeavors have not yet resulted in an equal distribution of Living SOC in urban and rural areas. Many residents feel the disparity of Living SOC is greatest in areas where the rural city was incorporated into an adjacent urban city—often called an integrated urban–rural city—or when new towns were developed in rural cities. This situation can lead to the creation of dissimilar landscapes and infrastructure within a single city. As such, this research observes how the resultant disparity in proximity between urban and rural areas impacts neighborhood satisfaction, as reflected in housing prices. The outcomes indicate that, with the exception of senior care and education, all observed amenities were found to be located closer to residences in urban areas. Addition",25.0,https://link.springer.com/content/pdf/10.1007/s10901-023-10098-y.pdf 8d2c1a851fa02d756efeb74af73165a5d9e44998,10.1109/istas50296.2020.9462170,Bidirectional Transformer based on online Text-based information to Implement Convolutional Neural Network Model For Secure Business Investment,2020.0,International Symposium on Technology and Society,Maryam Heidari; Setareh Rafatirad,"Real estate investment decisions are critical for low-income people who have just one home as their life-time investment option. So during the COVID-19 pandemic, unemployment causes many homeowners with a low income to lose their homes because of two major factors: one, they could not pay their mortgages without a job, and second, their house could not be rented easily. Rent prediction in real-estate can guarantee the success of an investment. Online information from real estate websites plays a significant role in making a business decision to buy a home. This paper applies natural language processing models to introduce a new model for safe real estate investment based on online information. For the first time, we use a transfer learning model based on online information from various online resources to detect a profitable rental property. Bidirectional Encoder Representations from Transformers(BERT) are used to implement a semantic convolutional neural network model to predict real estate investment safety. This work introduces a new model for rent prediction based on the United States housing market. Our contribution is three-fold: (1) using natural language processing approach",25.0, de6055fe7364e959989833492971c0a25fa95232,10.3390/buildings7040091,Immigrants and the City: The Relevance of Immigration on Housing Price Gradient,2017.0,,V. Antoniucci; G. Marella,"Foreign citizens are a more and more significant part of the population of Italian cities and society (8% of the country’s total population), and they contribute to changes in the cultural, social, and economic structure of the country. Our aim was to assess the incidence of the immigrant population on urban house price polarization, as measured using an original indicator: the center-periphery housing price gradient. While there is ample literature on the relationship between average prices and immigrant populations, less research has been conducted on immigration and the housing price gradient on a national scale. This price gradient may indicate whether immigration contributes to changing the residential market, also possibly revealing segregation phenomena. We ran multivariate regressions in several steps on an original dataset of housing prices and socio-economic factors concerning 112 Italian provincial capitals to elucidate whether immigration is correlated with the housing market divide. Our main findings confirmed that larger immigrant populations coincide with steeper housing price gradients on a national scale. Our tests also demonstrated that the relevance of this pheno",25.0,https://www.mdpi.com/2075-5309/7/4/91/pdf?version=1507975221 b5d93b6944c423820328917db3980e641a74fc13,10.47004/wp.cem.2021.3921,Linear panel regressions with two-way unobserved heterogeneity,2021.0,Journal of Econometrics,Hugo Freeman; M. Weidner,"We study linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known functional form (additive or multiplicative). In this paper, we allow for this functional form to be more general and unknown. We discuss two different estimation approaches that allow consistent estimation of the regression parameters in this setting as the number of individuals and the number of time periods grow to infinity. The first approach uses the interactive fixed effect estimator in Bai (2009), which is still applicable here, as long as the number of factors in the estimation grows asymptotically. The second approach first discretizes the two-way unobserved heterogeneity (similar to what Bonhomme, Lamadon and Manresa 2021 are doing for one-way heterogeneity) and then estimates a simple linear fixed effect model with additive two-way grouped fixed effects. For both estimation methods we obtain asymptotic convergence results, perform Monte Carlo simulations, and employ the estimators in an empiric",25.0,https://doi.org/10.47004/wp.cem.2021.3921 de48aa9a311d709ef0a33453d7042001c2e721a6,10.1111/jors.12524,"Public transport, noise complaints, and housing: Evidence from sentiment analysis in Singapore",2021.0,Journal of Regional Science,Yi Fan; Ho-Pin Teo; Wayne Xinwei Wan,"This paper investigates the effect of a new bus route on noise complaints of residents and the influence of noise on housing price. To overcome the challenge of mapping noise data with subjective emotion, we use a novel data source — text ‐ based noise complaint records from residents in a town in Singapore — and apply natural language processing tools to conduct sentiment analysis. To address the endogeneity concern regarding the bus route, we use a hypothetical least cost path as an instrument for the existing bus route. We find that living closer to the bus route for every 100 m increases noise complaints by around 10 percentage points, and the effect is more severe on medium floor levels (5th – 8th floors) and near bus stops (within 100 m). We further link noise with housing price and discover a price reduction of 3% with a 1 ‐ scale ‐ point increase in noise complaints. This implies that bus noise offsets 17.8% of the benefit from convenience, which sheds light on the importance of noise insulation in transit ‐ oriented developments. propose a novel methodology to measure the quasi real ‐ time noise sentiment at finer detailed level using the noise complaints data, which natur",25.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jors.12524 8df2886825daae6914b2c79be065562d76343302,10.3390/urbansci5040077,Why House Prices Increase in the COVID-19 Recession: A Five-Country Empirical Study on the Real Interest Rate Hypothesis,2021.0,Urban Science,Chung Yim Edward Yiu,"There are substantial rebounds in house prices in many developed economies after the outbreak of COVID-19. It provides a special opportunity to test the real interest rate hypothesis empirically as a “synchronized” price rebound implies a common cause of house price hikes across the economies. This study conducts a panel regression analysis on five economies, namely Australia, Canada, European Union, New Zealand, the United Kingdom, and the United States of America, to test the hypothesis. The data range from 2017Q1 to 2021Q1. The results confirm that the real interest rate imposes a negative and significant effect on house price growth rate after controlling for economic growth factors, unemployment factors, and cross-country fixed effects. The empirical result of the five housing markets shows that a 1% fall in the real interest rate caused a 1.5% increase in house prices, ceteris paribus, in this period. It also provides casual evidence refuting the economic growth hypothesis and the migrant hypothesis in New Zealand. The results provide far-reaching practical implications on housing policy and on the ways forward to solve housing affordability problems.",25.0,https://www.mdpi.com/2413-8851/5/4/77/pdf?version=1633658940 97421045d3435f359df5f0fa478cd3042fe8603d,10.2139/ssrn.2886481,Absolute income inequality and rising house prices,2016.0,Socio-Economic Review,Thomas Goda; C. Stewart; A. Torres-García," Income inequality and house prices have risen sharply in developed countries during 1975–2010. In line with theoretical models, we argue that this co-movement is no coincidence, but that inequality has driven up house prices on the grounds that it raises the aggregate demand for housing. Our results suggest that absolute inequality and house prices in most Organisation for Economic Co-operation and Development (OECD) countries were positively correlated and cointegrated, whereas relative inequality and mean income were not significant long-run determinants. This finding indicates that the surge in OECD house prices in part can be explained by a top-income-induced increase in housing demand, and that it is important to consider the interaction of rising mean income and its relative distribution when studying potential correlates of house prices. Moreover, our results confirm previous findings that the short-term real interest rate also is an important correlate of house prices.",25.0,https://eprints.kingston.ac.uk/id/eprint/42976/6/Stewart-C-42976-AAM.pdf 8571217e4c5d585e73c1437393b32bef691150e0,10.1016/j.apgeog.2020.102271,Determining heterogeneity of residential location preferences of households in Belgium,2020.0,,Kasper Cockx; F. Canters,"Abstract Residential location choice behaviour is a key component of the complex and dynamic human-environment interactions driving changes in the urban landscape. The residential location choices that households make depend on their socio-economic characteristics, attitudes and life cycle stage as well as the location's properties. The objective of this research was to analyse residential location preferences for different types of households in Belgium by developing a residential location choice model based on the use of regression trees. For each preference group, a discrete choice model was estimated identifying the main factors influencing residential location choices, and how these differ depending on household characteristics. The data-driven approach proposed in this paper enables a transparent interpretation of the main household characteristics explaining differences in residential location choice behaviour, and of the varying effect of location characteristics on residential location choice. Results obtained with the model show that tenure status, education level, nationality and household type successfully discriminate heterogeneous residential location preference profi",25.0, e99c0b6efc74a48816f236b958a375d34da8bbe6,10.3390/su11184896,Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach,2019.0,Sustainability,P. Morano; P. Rosato; F. Tajani; B. Manganelli; F. Di Liddo,"The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016–2017. The ability to generate a “unique” functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies.",25.0,https://doi.org/10.3390/su11184896 b572c9c5529902e7f5bdf7b077c37d6cc9d14f0a,10.1080/21681376.2019.1643777,"House price affordability, the global financial crisis and the (ir)relevance of mortgage rates",2019.0,"Regional Studies, Regional Science",G. Squires; D. Webber,"ABSTRACT Although house prices and wages are both influenced by distinct factors that shape their own evolutions, they are also intrinsically connected through house price affordability. Further, macroeconomic policies centred around adjustments in the mortgage rate are of prime importance in ensuring that the housing market does not overheat. This study contributes to the understanding of the link between housing market affordability and mortgage rates by investigating this association across regions of New Zealand using quarterly data between 2000 and 2017. Applications of trajectory regression reveal that the global financial crisis affected regional house price affordability asymmetrically and there was no statistically significant correlation between house price affordability and mortgage rates.",25.0,https://www.tandfonline.com/doi/pdf/10.1080/21681376.2019.1643777?needAccess=true 421defd413371fe95b2af1fa77021882136db4fa,10.5089/9781475503548.001,Why House Price Indexes Differ: Measurement and Analysis,2012.0,Social Science Research Network,M. Silver,"A key element in the build-up to the global recession and subsequently was the movement in house price indexes (HPIs). These indexes are particularly prone to methodological and coverage differences which can undermine both within-country and cross-country economic analysis. The paper outlines key measurement issues and reports on empirical work using an international panel data set that (i) considers whether differences in HPI measurement matter and, if so, in what way, and (ii) revisits the measurement of global house price inflation and the modeling of the determinants of house price inflation using HPIs corrected for differences in measurement practice.",25.0,https://www.elibrary.imf.org/doc/IMF001/12809-9781475503548/12809-9781475503548/Other_formats/Source_PDF/12809-9781475541168.pdf 35335d6e7f690b2dc2dd3ab4bfbb3e36b0f3a18b,10.1109/acit50332.2020.9300074,"House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan",2020.0,"Automation, Control, and Information Technology",Maida Ahtesham; N. Bawany; Kiran Fatima,"House prices are a significant impression of the economy, and its value ranges are of great concerns for the clients and property dealers. Housing price escalate every year that eventually reinforced the need of strategy or technique that could predict house prices in future. There are certain factors that influence house prices including physical conditions, locations, number of bedrooms and others. Traditionally predictions are made on the basis of these factors. However such prediction methods require an appropriate knowledge and experience regarding this domain. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize housing prices. In this paper, Gradient Boosting Model XGBoost is utilized to predict housing prices. Publicly available dataset containing 38,961 records of Karachi city is attained from an Open Real Estate Portal of Pakistan. Lot of work has been done in predicting house prices across many countries, however very limited amount of work has been done for predicting house prices in Pakistan. Our proposed house price prediction model is able to predict 98% accuracy.",25.0, caa0d7c959640499d35d0aa03c4880cb4000d157,10.1080/14445921.2007.11104223,Cell Phone Tower Proximity Impacts on House Prices: A New Zealand Case Study,2007.0,,S. Bond,,25.0,http://www.prres.net/Papers/PRPRJ_No_1_2007_Bond.pdf d3f36ef60f12efc782fb49dfa63c4d11871d219b,10.1080/24694452.2025.2516091,GeoConformal Prediction: A Model-Agnostic Framework for Measuring the Uncertainty of Spatial Prediction,2024.0,Annals of the American Association of Geographers,Xiayin Lou; Peng Luo; Liqiu Meng,"Spatial prediction is a fundamental task in geography, providing essential data support for various scenarios. Recent advancements, empowered by the development of geospatial artificial intelligence (GeoAI), have primarily focused on improving prediction accuracy while overlooking reliable measurements of prediction uncertainty. Such measures are crucial for enhancing model trustworthiness and supporting responsible decision-making. To address this issue, we propose a model-agnostic uncertainty assessment method called GeoConformal Prediction (GeoCP). First, a simulation study is conducted to validate the usefulness of GeoCP. Then, we applied GeoCP to two classic spatial prediction cases, spatial regression and spatial interpolation, to evaluate its reliability. For the case of spatial regression, we used XGBoost to predict housing prices, followed by GeoCP to calculate uncertainty. Our results show that GeoCP achieved a coverage rate of 93.67 percent, whereas bootstrapping methods reached a maximum coverage of 81.00 percent after 2,000 runs. We then applied GeoCP for the case of spatial interpolation models. By comparing a GeoAI-based geostatistical model with a traditional geosta",24.0,https://www.tandfonline.com/doi/pdf/10.1080/24694452.2025.2516091?needAccess=true e6fc0d5875024992e333df394e5f825f548e5cd7,10.3390/f12121783,"Exploring the Determinants of Urban Green Space Utilization Based on Microblog Check-In Data in Shanghai, China",2021.0,Forests,Dan Chen; Xuewen Long; Zhigang Li; Chuan Liao; C. Xie; S. Che,"Urban green space has significant social, ecological, cultural and economic value. This study uses social media data to examine the spatiotemporal utilization of major parks in Shanghai and explore the determinants of their recreational attraction. Methods: Based on microblog check-in data between 2012 and 2018 across 17 parks in Shanghai, we investigated the patterns at different temporal scales (weekly, seasonal and annual) and across workdays and weekends by using log-linear regression models. Results: Our findings indicate that both internal and external factors affect park utilization. In particular, the presence of sports facilities significantly contributes to higher visit frequency. Factors such as the number of subway stations nearby, scenic quality and popularity have a positive impact on check-in numbers, while negative factors affecting park use are number of roads, ticket price and average surrounding housing price. Across different temporal scales, the use patterns of visitors have obvious seasonal and monthly tendencies, and the differences of workday and weekend models lie in external factors’ impacts. Conclusions: In order to achieve the goal of better serving the ",24.0,https://www.mdpi.com/1999-4907/12/12/1783/pdf?version=1639641768 bc3fe637239907e8702a37f95278511c61611329,10.3390/su11030669,"Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China",2019.0,Sustainability,Xiao-Qi Zhang; Yanqiao Zheng; Lei Sun; Qi-Wen Dai,"Using housing market data of Beijing and Hangzhou, China, we conduct a case study to detect how the difference of urban structure can affect the relationship between the subway system and housing prices. To quantify the characteristics of urban structure, we propose a constrained clustering method, which can not only reveal the spatial heterogeneity of the housing market, but also provides a link between heterogeneity and the underlying urban structure. Applying constrained clustering to Beijing and Hangzhou, we find that the relationship between accessibility to metro stations and housing prices is weak and vulnerable, while the improvement of commuting efficiency, measured by a key variable, the metro index, does have a robust connection to metro premium on housing units. In particular, only a large metro index can be associated with a positive metro premium. Structural features, such as the size of urban core and the existence of multiple sub-centers, influence the metro premium by affecting the value and spatial distribution of the metro index. The evidence from Beijing and Hangzhou supports that in a mono-centric city, the size of the urban core is positively associated with t",24.0,https://www.mdpi.com/2071-1050/11/3/669/pdf?version=1548663935 7565f7ec9aefc380cf897b8d5ea10d2d098d1500,10.3390/su12145679,Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices,2020.0,Sustainability,Yunjong Kim; Seung-Ho Choi; Mun Yong Yi,"In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and ",24.0,https://www.mdpi.com/2071-1050/12/14/5679/pdf?version=1595295841 a5dfcc57dbb1ed6dabcc0f15c4c987c2f2cc5c91,10.1007/s41324-018-0203-1,"Local determinants of household gas and electricity consumption in Randstad region, Netherlands: application of geographically weighted regression",2018.0,Spatial Information Research,B. Mashhoodi; A. van Timmeren,"The previous studies on household energy consumption (HEC) are based on an implicit assumption: the impact of geographic determinants on HEC is uniform across a given region, and such impacts could be unveiled regardless of geographic location of households in question. Consequently, these studies have searched for global determinants which explain HEC of all areas. This study aim at examining validity of this assumption in Randstad region by putting forward a question regarding households’ gas and electricity consumption: are the determinants global, stationary across all the areas of the region, or local, varying from one location to another? By application of geographically weighted regression, impact of socioeconomic, housing, land cover and morphological indicators on HEC is studied. It is established that the determinants of HEC are local. This result led to second question: what are the main determinants of gas and electricity consumption in different neighborhoods of Randstad? The results show that variety of factors could be the most effective determinant of gas consumption in different neighborhoods: building age, household size and inhabitants’ age, inhabitants’ income a",24.0,https://link.springer.com/content/pdf/10.1007/s41324-018-0203-1.pdf 0da16fcc25a4072e181978284964b10515347368,10.1016/j.ijinfomgt.2019.09.008,A novel architecture to identify locations for Real Estate Investment,2021.0,International Journal of Information Management,E. SandeepKumar; V. Talasila; R. Pasumarthy,"Abstract The identification of a favorable location for investment is a key aspect influencing the real estate market of a smart city. The number of factors that influence the identification easily runs into a few hundreds (including floor space area, crime in the locality and so on). Existing literature predominantly focuses on the analysis of price trends in a given location. This paper aims to develop a set of tools to compute an optimal location for investment, a problem which has received little attention in the literature (analysis of house price trends has received more attention). In previous work the authors proposed a machine learning approach for computing optimal locations. There are two main issues with the previous work. All real estate factors were assumed to be independent and identically distributed random variables. To address this, in the current paper we propose a network structure to derive the relational inferences between the factors. However, solving the location identification problem using only a network incurs computational burden. Hence, the machine learning layers from the previous work is combined with a network layer for computing an optimal location ",24.0, b5418cdf1a92c3410b0db715b382f01642d9b3c9,10.1016/j.apcbee.2014.10.059,The Impact of Landscape Design on House Prices and Values in Residential Development in Urban Areas,2014.0,,M. R. Hussain; I. Tukiman; I. Zen; Fitrynadia Mohd Shahli,"Focusing on a world class living environment, a good quality residential area can be highlighted as an important issue in creating a sustainable living environment. However, limited green spaces within the proximity of residential properties are not supporting the landscape space and are not conducive to living space. The aim of the paper is to investigate the impact of landscape design on house prices and values in residential development particularly in urban areas. The paper employed quantitative approaches which include i) a questionnaire survey; and ii) an observation. This empirical study is based on the findings of case studies conducted in several residential areas in Klang Valley. The findings show that the residents have very clear ideas on how much green space is really important in residential areas in order to create a sustainable residential environment. Despite the absence of public actions in terms of providing information, encouraging participations in the survey and promoting awareness of those interviewed have shown considerable interest in promoting landscape design to be applied in residential development. The perceptions of the quality and quantity of landscap",24.0,https://www.sciencedirect.com/science/article/pii/S2212670814002097/pdf e06a4d3e776b1e6666c940f56997612ef11873e4,10.3846/1648715x.2017.1409290,The effect of BEAM Plus certification on property price in Hong Kong,2017.0,,E. Hui; C. Tse; Ka Yu,"In response to the public’s increasing awareness of sustainable development, the construction industry has introduced “green” buildings which emphasize better environmental performance. However, as a building’s environmental performance is difficult to discern for laymen, different green building certifications have been established for evaluations in this regard. This study evaluates whether there exists a price premium for living space in buildings certified with BEAM Plus. The findings show that the prices of flats in BEAM Plus-certified buildings are 4.4% higher than those in nonregistered buildings, and that housing units in buildings with an “unclassified” rating are transacted at a discount of 5.9%. Nevertheless, if homebuyers mistake an “unclassified” building for a non-registered building, due to the non-disclosure of the “unclassified” result by developers, the price premium of BEAM Plus certification becomes higher (6.2%). Regardless, the price premium is much lower than those on the office sector. The reasons behind such differences can be attributed to the disparities in the tangible and intangible benefits associated with green living space and green office space. Pol",24.0,https://journals.vgtu.lt/index.php/IJSPM/article/download/1793/1412 67e47502fc32949afbe52c45d20b0e2070cfbd59,10.3390/bs12090327,"Analyzing the Housing Consumer Preferences via Analytic Hierarchy Process (AHP) in Dubai, United Arab Emirates",2022.0,Behavioral Science,Chuloh Jung; Nahla Al Qassimi; Naglaa Sami Abdelaziz Mahmoud; S. Lee,"Dubai was one of the top three real estate destinations in the world for investment in 2020. This paper aims to understand the order of preference for various housing determinants by housing consumers in Dubai. As a methodology, a survey was conducted on Dubai residents, and Analytic Hierarchy Process (AHP) was performed to identify the housing determinants and consumers’ preferences. In addition, the respondents’ demographic characteristics identified priorities by income, place of residence, age, gender, and type of house. The results showed that housing consumers place importance on housing price and rent (0.0918), and the investment value (0.0866). However, there was no serious consideration for social and psychological factors, other than safety (0.0730). Regarding gender, men place more importance on the housing price and rent (0.113), and the investment value (0.110). In comparison, women place more importance on factors such as the convenience of transportation (0.104), safety (0.093), and residential environment (0.082). In the age groups, the interest in the educational environment (0.081) among the 40-year-olds was relatively high. In terms of monthly income, the higher ",24.0,https://www.mdpi.com/2076-328X/12/9/327/pdf?version=1662718396 a4ca91ce532e2851185221577983efeff14a58db,10.3390/su12218895,Tweeting the High Line Life: A Social Media Lens on Urban Green Spaces,2020.0,Sustainability,Jisoo Sim; P. Miller; Samarth Swarup,"The objective of this study is to investigate elevated parks as urban green spaces using social media data analytics. Two popular elevated parks, the High Line Park in New York and the 606 in Chicago, were selected as the study sites. Tweets mentioning the two parks were collected from 2015 to 2019. By using text mining, social media users’ sentiments and conveyed perceptions about the elevated parks were studied. In addition, users’ activities and their satisfaction were analyzed. For the 606, users mainly enjoyed the free events at the park and worried about possible increases in housing prices and taxes because of the 606. They tended to participate in physical activities such as biking and walking. Although the 606 provides scenic observation points, users did not seem to enjoy these. Regarding the High Line, users frequently mentioned New York City, which is an important aspect of the identity of the park. The High Line users also frequently mentioned arts and relaxation. Overall, this study supports the idea that social media analytics can be used to gain an understanding of the public’s use of urban green spaces and their attitudes and concerns.",24.0,https://www.mdpi.com/2071-1050/12/21/8895/pdf?version=1604371663 8199a4a17b07669311c1a6c3bc94ff0597a6b05a,10.2139/ssrn.3468520,Long Shadow of Racial Discrimination: Evidence from Housing Covenants of Minneapolis,2019.0,Social Science Research Network,A. Sood; William Speagle; Kevin Ehrman-Solberg,"This paper studies the effect of racially-restrictive covenants prevalent during the early-to-mid 20th century on present-day socioeconomic outcomes such as house prices and racial segregation. Racial covenants were clauses in property deeds that prohibited the sale or renting of a property to specific religious and ethnic minorities. Using a newly created geographic data set of over 30,000 historical property deeds (1910-1955) from Hennepin County in Minnesota, we exploit the unanticipated 1948 Supreme Court ruling that made racially-restrictive covenants unenforceable. We employ a fuzzy regression discontinuity around the ruling to document that racial covenants have had time-persistent effects and have significantly affected the socioeconomic geography of Minneapolis and adjoining areas. In particular, we document that houses that were covenanted have on average 4-15% higher present-day prices compared to properties which were not covenanted. We also find that census blocks with larger share of covenanted lots have smaller black population and lower black home ownership rates.",24.0,https://doi.org/10.2139/ssrn.3468520 def83ba0d419d5d7617f2227854e572cb76ee681,10.2139/ssrn.3729072,Why are Residential Property Tax Rates Regressive?,2020.0,Social Science Research Network,Natee Amornsiripanitch,"Among single family homes that enjoy the same set of property tax-funded amenities and pay the same statutory property tax rate, owners of cheap houses pay 50% higher effective tax rates than owners of expensive houses. This pattern appears throughout the United States and is caused by systematic assessment regressivity -- cheap houses are overappraised relative to expensive houses. At least 30% of the observed regressivity can be explained by tax assessors' flawed valuation methods, which ignore variation in priced house and neighborhood characteristics. Infrequent reappraisal explains less than 10% of the phenomenon. Heterogeneous appeal behavior and outcomes do not contribute. Overtaxation of minorities and low-income households is a by-product of assessment regressivity because these households sort into cheap houses. Within the same local house price decile, black households are proportionately taxed, while Hispanic and low-income households are undertaxed. Taken at face value, correcting assessment regressivity would increase poor homeowners' net worth by more than 15%.",24.0,https://doi.org/10.21799/frbp.wp.2022.02 3db01bac6d153608cb3a370bba9afc622ae6f889,10.1002/soej.12341,Measuring Heterogeneous Price Effects for Home Acquisition Programs in At‐Risk Regions,2019.0,Southern Economic Journal,E. Frimpong; J. Kruse; Gregory Howard; R. Davidson; J. Trainor; L. Nozick,"Any entity offering flood insurance, whether it is private or government‐administered such as the National Flood Insurance Program (NFIP), faces the challenge of solvency. This is especially true for the NFIP, where homeowner affordability criteria limit the opportunity to charge fully risk‐based premiums. One solution is to remove the highest flood risk properties from the insurer's book of business. Acquisition (buyout) of flood‐prone structures is a potentially permanent solution that eliminates the highest risk properties while providing homeowners with financial assistance to relocate in a less risky location. To encourage participation, homeowners are offered a preflood fair market value of their damaged (or at risk of damage) structures. Although many factors have been shown to affect a homeowner's decision to accept an acquisition offer, very little research has been devoted to the influence of price or monetary incentive offered on homeowners' willingness to participate in acquisition programs. We estimate a pooled probit model and employ a bootstrap methodology to determine the effects of hypothetical home price offers on homeowners' acquisition decisions. We do so while ",24.0, df0fcdc0b0fce3abbe9270fe79fb0dfb553b8f44,10.1016/j.landusepol.2018.02.056,Open space amenities and residential land use: An Australian perspective,2018.0,Land Use Policy,M. Jones; R. Reed,"In a competitive market the individuals will bid up house prices to a level reflecting the capitalised value of the benefits that property owners perceive they receive from the presence of amenities, however there have been relatively few recent studies into the relationship between house prices and active open space. This is an important area of research as active open space helps to address some of the high profile and emerging problems in society including lack of exercise, obesity and a sedentary lifestyle. In the findings the distance to active open space was found to have a significant relationship with the level of house prices. When distance was broken down into 100 m intervals, associations differed in terms of their direction and strength. Since demand for access to active open space outweighs the demand for passive open space, the findings confirm that large-scale residential developments need the assistance of planning authorities to revise policy in response to changing community needs.",24.0,https://figshare.com/articles/journal_contribution/Open_space_amenities_and_residential_land_use_an_Australian_perspective/20807695 dcabd0b46a12af41e65b932b18655506f76279a1,10.1080/13683500.2023.2204398,Tourism activity affects house price dynamics? Evidence for countries dependent on tourism,2023.0,Current Issues in Tourism,Susana Cró; António Miguel Martins,"ABSTRACT This study analyses the impact of tourism in house prices in eight tourism-dependent countries in terms of exports for the period from 2000 to 2018. We employ a Vector-Error Correction Model (VECM) for the empirical estimation given that house prices, tourism activity and other determinants are cointegrated. The results indicate that tourism has a significant positive impact on house prices both in the short-run and in the long-run. The Granger causality tests show that tourism activity Granger-causes house prices in the eight countries analysed. These results have important practical and political implications. There is a delicate environment and social equilibrium in tourist destinations that it is necessary to ensure. If it is true that tourism has positive effects on the economy through job creation and economic growth it is also important to internalize the negative externalities caused by tourism, through the adoption of appropriate economic instruments and policies.",24.0,https://doi.org/10.1080/13683500.2023.2204398 b4dd4f693c3c9fb6fe4a70a3f344547c503a4d21,10.1177/0042098019871191,The shapes of US cities: Revisiting the classic population density functions using crowdsourced geospatial data,2020.0,Urban studies,Y. Qiang; Jinwen Xu; Guohui Zhang,"The declining pattern of population density from city centres to the outskirts has been widely observed in American cities. Such a pattern reflects a trade-off between housing price/commuting cost and employment. However, most previous studies in urban population density functions are based on the Euclidean distance, and do not consider commuting cost in cities. This study provides an empirical evaluation of the classic population density functions in 382 metropolitan statistical areas (MSA) in the USA using travel times to city centres as the independent variable. The major findings of the study are: (1) the negative exponential function has the overall best fit for population density in the MSAs; (2) the Gaussian and exponential functions tend to fit larger MSAs, while the power function has better performance for small MSAs; (3) most of the MSAs appear to show a decentralisation trend during 1990–2016, and larger MSAs tend to have a higher rate of decentralisation. This study leverages crowdsourced geospatial data to provide empirical evidence of the classic urban economic models. The findings will increase our understanding about urban morphology, population–job displacement an",24.0, 9c6b86f52f2529a9b970503dc6d5fdf73cf93bc3,10.68479/001c.8047,Explaining Differences in State Unemployment Rates During the Great Recession,2012.0,Journal of Regional Analysis & Policy,Michael L. Walden,"The deterioration of the labor market during the Great Recession has varied considerably among states. This study investigates determinants of several measures of the change in state unemployment rates from pre-recessionary lows to recessionary peaks. As in previous studies for earlier recessions, industrial composition – specifically the share of state GDP (gross domestic product) in manufacturing – was strongly associated with greater labor market deterioration. However, new to this study were the findings that larger declines in housing prices and bigger household in-migration rates were also associated with more state labor market decline during the Great Recession. Three geographic groups of states – in the Far West, the Southeast, and the Midwest – had economic characteristics resulting in greater increases in unemployment rates during the Great Recession.",24.0,https://jrap.scholasticahq.com/article/8047.pdf 541ff75a6f2ea155deede883a961d298c5dd12fc,10.1016/j.ecolecon.2019.05.021,Environmental amenities and quality of life across the United States,2019.0,Ecological Economics,Mona Ahmadiani; S. Ferreira,"Abstract This paper investigates the spatial variation in subjective well-being across the United States. We match individual-level survey data from the Behavioral Risk Factor Surveillance System (BRFSS) that includes a life-satisfaction question, to county-level local amenities between 2005 and 2010. We show that life satisfaction varies widely across U.S. counties (even if these are in the same state and after controlling for individual characteristics), which suggests that housing price and wage differentials are not fully compensating for differences in amenities across locations. We also show that local amenities including climate, geography, environmental externalities, and other local public goods, explain a sizable fraction of this variation, stressing the importance of environmental management across space.",24.0,https://doi.org/10.2139/ssrn.2906885 82c7b324fecd20c1cea390847e95e61572ffe06c,10.1109/iccmc56507.2023.10084197,House Price Prediction using Machine Learning Algorithm,2023.0,International Conference Computing Methodologies and Communication,Shailendra Sharma; Deepti Arora; Gori Shankar; Priyanka Sharma; Vihaan Motwani,"Houses are one of the fundamental needs in human society. Good and peaceful surroundings where people feel comfortable are called houses. Hence, to have a good and happy living, people need to choose a good house model. This article focuses on accurately predicting house prices by using machine learning algorithms. This model helps people in selecting the house that is suitable for their living. The main parameter people look for will be the surrounding area, house type, price, location, and some other amenities. All these factors are considered to find out the required house for living. As people are very concerned about their budgets for buying a house, the prediction of house prices should be very precise. It also helps people in choosing houses based on their budgets, which do not affect their financial state in the future. The main outcome of this model is to predict the price of a house accurately as per the user requirements. This study has attempted to implement various machine learning algorithms like Linear Regression (LR), Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random Forest (RF) Regressor algorithms. Finally, the algorithm that gen",24.0, 35083c70cbdfa6e1587519a0c76870abfb12a102,10.18070/euiibfd.42448,HEDONIC ESTIMATION OF HOUSING MARKET PRICES IN TURKEY,2014.0,,Rüştü Yayar; Derya Demir,"Bu calismada, Turkiye konut sektorunde apartman dairelerinin fiyatini etkileyen faktorlerin belirlenmesi amaclanmistir Model sonuclarina gore, konutlarda havuz, jakuzi ve su deposu bulunmasi, konutun dubleks daire olmasi, isi sisteminin musterek/merkezi olmasi, konutun buyuklugu, banyo zemininin marley ya da PVC olmasi, banka hizmetlerine ve zorunlu egitim hizmetlerine yakin olmasi, kablolu yayin, telefon hatti ve garaj bulunmasi konutun degerini artirmaktadir. Konutun bodrum ya da zemin kat olmasi, binanin yapim tarihinin eski olmasi, oda zemininin karo seramik olmasi, banyo zemininin sap (kara beton) olmasi, kullanilan yakitin komur olmasi konutun degerini dusurmektedir",24.0,https://doi.org/10.18070/euiibfd.42448 c147cda8bf83b3142649ae6e60fef7e1b53baf59,10.1109/icesc48915.2020.9155839,Prediction of House Pricing using Machine Learning with Python,2020.0,"International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]",Mansi Jain; Himani Rajput; Neha Garg; Pronika Chawla,This paper provides an overview about how to predict house costs utilizing different regression methods with the assistance of python libraries. The proposed technique considered the more refined aspects used for the calculation of house price and provide the more accurate prediction. It also provides a brief about various graphical and numerical techniques which will be required to predict the price of a house. This paper contains what and how the house pricing model works with the help of machine learning and which dataset is used in our proposed model.,24.0, aa3e7bc90f5c74d83e16dca909464199c9e0d002,10.1016/j.jhe.2017.02.003,The effect of local amenities on house price appreciation amid market shocks: The case of school quality,2017.0,,Mitchell R. Livy,,24.0, 98e296c6406bf1c0e9d784c52731c92d28c56ce1,10.1108/17538270910963090,"Housing price determinants in Istanbul, Turkey: An application of the classification and regression tree model",2009.0,,Onur Özsoy; H. Sahin,,24.0, a8dc0bc206dcf71c0dec7e3afac8ab5c1e1c53f5,10.1109/tifs.2021.3087332,With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models,2020.0,IEEE Transactions on Information Forensics and Security,Jia-Lin Wen; Benjamin Zi Hao Zhao; Min-Hui Xue; Alina Oprea; Haifeng Qian,"With the rise of third parties in the machine learning pipeline, the service provider in “Machine Learning as a Service” (MLaaS), or external data contributors in online learning, or the retraining of existing models, the need to ensure the security of the resulting machine learning models has become an increasingly important topic. The security community has demonstrated that without transparency of the data and the resulting model, there exist many potential security risks, with new risks constantly being discovered. In this paper, we focus on one of these security risks – poisoning attacks. Specifically, we analyze how attackers may interfere with the results of regression learning by poisoning the training datasets. To this end, we analyze and develop a new poisoning attack algorithm. Our attack, termed Nopt, in contrast with previous poisoning attack algorithms, can produce larger errors with the same proportion of poisoning data-points. Furthermore, we also significantly improve the state-of-the-art defense algorithm, termed TRIM, proposed by Jagielsk et al. (IEEE S&P 2018), by incorporating the concept of probability estimation of clean data-points into the algorithm. Our ne",23.0,https://arxiv.org/pdf/2006.11928 56d0580efd57a6afd6243eccdccf2e3c1cf59d2b,10.1016/j.habitatint.2017.06.005,Measuring the degree of speculation in the residential housing market: A spatial econometric model and its application in China,2017.0,,Xiao-Dong Yang; Yong-Xiang Wu; Q. Shen; H. Dang,"Abstract Since the housing marketization reform, China's real estate industry has rapidly developed and commercial housing prices have risen sharply. The main reason for this is the speculative demand for housing, which breaks the equilibrium of supply and demand, leading to housing prices deviating from their basic value. The housing market in China is not isolated by province, since speculative behavior in one part of the country can affect other regions. This paper analyzes the spatial relevance in housing prices among different provinces in China by calculating Moran's I index and by measuring the speculation degree through spatial autoregressive model (SAR), a spatial economic model combined with a spatial weight matrix. For commercial housing speculation degree measurement and comparison, 31 provinces in China were chosen along with the following variables: housing prices, personal disposable income, one-year personal housing mortgage rates, housing prices growing rates, the rent, amount of residential investment, and the construction areas of houses. The results show that China's housing prices have a clear interaction among the selected cities, and that housing speculation ",23.0,http://ira.lib.polyu.edu.hk/bitstream/10397/70292/1/Yang_Xiaodong_2017.pdf 0ecc4119e914fb2f076918dfe077bfd4dffb105e,10.3390/land13040480,Assessing Spatial Heterogeneity in Urban Park Vitality for a Sustainable Built Environment: A Case Study of Changsha,2024.0,Land,Liwei Qin; Wenke Zong; Kai Peng; Rongpeng Zhang,"In the realm of sustainable city development, evaluating the spatial vitality of urban green spaces (UGS) has become increasingly pivotal for assessing public space quality. This study delves into the spatial heterogeneity of park vitality across diverse urban landscapes at a city scale, addressing limitations inherent in conventional approaches to understanding the dynamics of park vitality. Leveraging geotagged check-in data from 65 parks in the study case of Changsha City, a quantitative analysis was undertaken to assess spatial vitality. The investigation incorporated data concerning internal and external factors influencing park vitality, employing the Multi-scale Geographically Weighted Regression (MGWR) model to dissect nuanced spatial heterogeneity. The research uncovers notable spatial discrepancies in factors influencing park vitality across diverse urban areas, emphasizing the reliance on adjacent residential communities and internal commercial amenities provision. These dependencies correspond with economic development differences among urban locales, revealing distinct geographic trends. This study has a novel perspective and methodology for investigating urban park vi",23.0,https://www.mdpi.com/2073-445X/13/4/480/pdf?version=1712588691 34db1269fff24b31c412e71c171f43d213287b69,10.7307/ptt.v35i1.67,"How Do Land Use, Built Environment and Transportation Facilities Affect Bike-Sharing Trip Destinations?",2023.0,Promet,Ahmed Jaber; Bálint Csonka,"The purpose of this research is to investigate the effect of land use, built environment and public transportation facilities’ locations on destinations of bike-sharing trips in an urban setting. Several methods have been applied to determine the relationship between predicting variables and trip destinations, such as ordinary least squares regression, spatial regression and geographically weighted regression. Additionally, a comparison between the proposed models, count models and random forest has been conducted. The data were collected in Budapest, Hungary. It has been found that touristic points of interest, and healthcare and educational points have a positive impact on bike-sharing destinations. Public transportation stops for buses, trains and trams attract bike-sharing users, which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial and educational zones. Other variables, such as road length and water areas, form as constraints to bike-sharing trip destinations. Geographically weighted and spatia",23.0,http://traffic2.fpz.hr/index.php/PROMTT/article/download/67/48 772afc3ec95144862db2a66aa6b3acb010fb4f56,10.1177/1748895819874868,The influence of urban built environment on residential burglary in China: Testing the encounter and enclosure hypotheses,2019.0,Criminology & Criminal Justice,Han Yue; Xin-Yan Zhu,"It is widely recognized that crimes are clustered in space. Features of the built environment are among various factors associated with crime clustering patterns. There are two competing perspectives on this association: the “encounter” and “enclosure” hypotheses. This study examines the effect of the built environment on the spatial distribution of residential burglary in Wuhan, China, aiming to test these two competing perspectives. First, we review existing studies analyzing the influence of the built environment on residential burglary, especially those based on the two hypotheses. We then conduct an exploratory data analysis to observe the spatial and temporal distribution patterns of residential burglaries. Next, we use a multivariate spatial regression model to evaluate the influence of various built environment characteristics on residential burglaries, after controlling for socio-demographic variables, guardianship, and the time-of-day effect. The results suggest a double peak in the hourly distribution of burglaries: 6:00 a.m.–8:59 a.m. and 5:00 p.m.–7:59 p.m. Burglaries are positively and significantly autocorrelated in space and mainly concentrated in the north-west and",23.0, f23a786a9cdac4ec93e7ebff583be100aa5d6514,10.5379/urbani-izziv-en-2011-22-02-003,"Does Comprehensive Redevelopment Change the Housing Price Gradient? A Case Study in Mongkok, Hong Kong",2009.0,,Y. Yau,"Urban decay is an inevitable result of the growth of every city, and Hong Kong is not an exception. Many buildings were found to be in dilapidation, and there is an urgency to tackle urban decay in the city. Redeveloping dilapidated buildings has long been regarded as an effective solution to this urban problem. Yet, as suggested in the literature, redevelopment may be liable for gentrifying the neighbourhood because it pushes upon the property prices and rentals in the vicinity of redeveloped sites. However, empirical studies on how comprehensive redevelopment affects housing values in the neighourhood are not common. In this light, this research investigates the impacts of Argyle Street/Shanghai Street Redevelopment Project undertaken by the Urban Renewal Authority on the sale prices of housing nearby. A panel data is employed and the change in the spatial price gradient before and after the redevelopment project is explored. The analysis results suggest that proximity to the project site had a significant positive impact with housing prices before the project. Yet, no change in the spatial-price gradient is spotted after the completion of the project. These results confirm that ",23.0,https://doi.org/10.5379/urbani-izziv-en-2011-22-02-003 b22ca61bdc73f81cb7a87917fa781f947ce099e6,10.3390/ijerph20085440,"The Geography of Mental Health, Urbanicity, and Affluence",2023.0,International Journal of Environmental Research and Public Health,J. Cortina; Shana Hardin,"Residential location has been shown to significantly impact mental health, with individuals in rural communities experiencing poorer mental health compared to those in urban areas. However, the influence of an individual’s social group on the relationship between residential location and mental health outcomes remains unclear. This study disaggregates the rural-urban binary and investigates how geography and social groupings interact to shape mental health outcomes. Merging data from PLACES and Claritas PRIZM, we conducted a hotspot analysis, generated bivariate choropleth maps, and applied multiscale geographically weighted regressions to examine the spatial distribution of mental health and social groupings. Our findings reveal that mental health is influenced by complex interactions, with social groups playing a critical role. Our study highlights that not all rural and urban areas are alike, and the extent to which social groups influence mental health outcomes varies within and across these areas. These results underscore the need for policies that are tailored to meet the unique mental health needs of individuals from different social groups in specific geographic locations t",23.0,https://www.mdpi.com/1660-4601/20/8/5440/pdf?version=1680869559 5b6ff1adff576b8c61b57c55bc5e205884530223,10.21837/pmjournal.v14.i5.186,IDENTIFYING FACTORS INFLUENCING URBAN SPATIAL GROWTH FOR THE GEORGE TOWN CONURBATION,2016.0,PLANNING MALAYSIA,M. A. Mahamud; Narimah Samat; Noorzailawati Mohd Noor,"Urban growth, which caused spatial land use and land cover changes has affected various physical environment, social, and economic activities. Thus, in order to understand the dynamic process of urban spatial growth, researchers throughout the world have implemented diverse approaches, where spatial models have been developed to predict and simulate future urban growth. Those models were developed based on the driving forces that stimulate urban spatial growth. Therefore, in ensuring reliable models to be developed will be able to forecast future changes and their potential environmental effects, the driving forces must be identified. The objective of this paper is to identify possible driving forces that promote urban spatial growth of the George Town Conurbation. The study was conducted based on reviewing recent publications in journals and an on-line survey. An on-line survey was generated and distributed to academicians and urban planners to identify factors influencing urban spatial growth and their weights. The findings indicated that distance to public amenities, cheap housing price, and distance to the workplace are among factors that are important determinants of urban dev",23.0,http://www.planningmalaysia.org/index.php/pmj/article/download/186/180 48517fb02c92af139f94cfc8310cfc8697ce8410,10.2478/zireb-2018-0015,Macroeconomic and Demographic Determinants of Residential Property Prices in Malaysia,2018.0,Zagreb International Review of Economics and Business,I. Trofimov; Nazaria Md. Aris; Dickson C. D. Xuan,"Abstract This paper studies the relationship between residential property prices and macroeconomic and demographic determinants in Malaysia. In the years following the Asian financial crisis, property prices in Malaysia rose substantially, resulting in an affordability crisis and ultimately policy responses to the problem. Using unit root, Johansen-Juselius cointegration, VECM-based Granger causality tests and variance decomposition, and considering quarterly data that covers 2000-2015 period, we established that residential property price growth is principally driven by strong demographic performance and population growth and is backed by the low interest rate environment and rising consumer prices. Household income and level of GDP do not appear to contribute to property price growth. Certain distortions and asymmetries in the Malaysian real estate markets are documented: oversupply in the higher price segment of the market coupled with the lack of affordable housing in the lower price segment; household income growth lagging behind GDP and property price growth, thereby dampening housing demand; growing rental markets in major urban areas as a result of the affordability crisis;",23.0,https://www.sciendo.com/pdf/10.2478/zireb-2018-0015 ba48c13ae125f1895e4ea41405199dc1b200d247,10.1080/24694452.2023.2227683,From Proximity to Quality: The Capitalization of Public Facilities into Housing Prices,2023.0,Annals of the American Association of Geographers,D. Tong; Yue Shen; Xiao-Guang Wang; Yiyu Sun; I. MacLachlan; Xin Li,"Housing prices are significantly influenced by the presence of public facilities, such as schools, parks, and transport infrastructure. Whereas existing literature has mainly focused on the proximity of public facilities, this study goes beyond proximity and introduces the concept of quality metrics to evaluate public facilities. By employing the gradient-boosting decision trees approach, we analyze the nonlinear relationships between public facilities and property values in Shenzhen, China. Our study not only quantifies the extent to which the quality of these facilities is capitalized in housing prices, but also examines the interaction effects of quality and proximity on housing prices. Our results reveal that quality variables exhibit a greater relative importance than proximity variables in determining housing prices, and this relationship follows a nonlinear pattern. Furthermore, we investigate the moderating effects of quality on the relationship between proximity and housing prices. We find that the amplifying effects of higher quality are particularly evident in metro stations and public middle schools, whereas the impact of park quality on housing prices is less pronounce",23.0, 9b98208c5f3b695f09f18f67a2c3b46b7b6ea3a3,10.1016/j.jmva.2017.09.006,Estimation and variable selection for quantile partially linear single-index models,2017.0,Journal of Multivariate Analysis,Yuankun Zhang; H. Lian; Yan Yu,"Partially linear single-index models are flexible dimension reduction semiparametric tools yet still retain ease of interpretability as linear models. This paper is concerned with the estimation and variable selection for partially linear single-index quantile regression models. Polynomial splines are used to estimate the unknown link function. We first establish the asymptotic properties of the quantile regression estimators. For feature selection, we adopt the smoothly clipped absolute deviation penalty (SCAD) approach to select simultaneously single-index variables and partially linear variables. We show that the regularized variable selection estimators are consistent and possess oracle properties. The consistency and oracle properties are also established under the proposed linear approximation of the nonparametric link function that facilitates fast computation. Furthermore, we show that the proposed SCAD tuning parameter selectors via the Schwarz information criterion can consistently identify the true model. Monte Carlo studies and an application to Boston Housing price data are presented to illustrate the proposed approach.",23.0,https://www.sciencedirect.com/science/article/pii/S0047259X17305626/pdf 6138ff281fe4f3e91e75cbbfdbd9c0cc0fd86524,10.1016/j.cities.2020.103019,Who believes and why they believe: Individual perception of public housing and housing price depreciation,2020.0,,Ayoung Woo; Kenneth Joh; Chia-Yuan Yu,"Abstract NIMBY attitudes have been a longstanding obstacle to the implementation of public housing programs. The root cause of community opposition to public housing has primarily been the fear of housing price depreciation. This research extends the previous literature by examining individual perceptions and beliefs in the impact of public housing on neighboring housing prices. This study examines specific characteristics of residents that affect their negative beliefs in the change of housing prices due to public housing in Seoul, Korea. We use a logistic regression method to examine significant characteristics that influence the belief of housing price depreciation due to public housing developments. The findings showed that variables including income category, apartment living, negative physical image of public housing, positive and negative environmental changes due to public housing, and socioeconomically advantaged neighborhoods were positively associated with the belief of housing price depreciation due to public housing. Conversely, female gender, older age, self-employed workers, existence of public housing, and preference for the mixed income approach were negatively cor",23.0, b3dd97435ee56694c0c5b353fb98756cf56e80f2,10.1111/j.1467-9485.2012.00593.x,The Impact of Population Ageing on House Prices: A Micro-Simulation Approach,2012.0,Social Science Research Network,Yu Chen; K. Gibb; Chris Leishman; R. Wright,"This paper attempts to estimate the impact of population ageing on house prices. There is considerable debate about whether population ageing puts downwards or upwards pressure on house prices. The empirical approach differs from earlier studies of this relationship, which are mainly regression analyses of macro time-series data. A micro-simulation methodology is adopted that combines a macro-level house price model with a micro-level household formation model. The case study is Scotland, a country that is expected to age rapidly in the future. The parameters of the household formation model are estimated with panel data from the British Household Panel Survey covering the period 1999-2008. The estimates are then used to carry out a set of simulations. The simulations are based on a set of population projections that represent a considerable range in the rate of population ageing. The main finding from the simulations is that population ageing – or more generally changes in age structure – is not likely a main determinant of house prices, at least in Scotland.",23.0,http://www.strath.ac.uk/media/1newwebsite/departmentsubject/economics/research/researchdiscussionpapers/2012/12-07-Final.pdf 77e6003974c60c1148b961627fec6be3fc198f42,10.2139/ssrn.2796934,Assessing House Prices in Germany: Evidence from an Estimated Stock-Flow Model Using Regional Data,2013.0,Social Science Research Network,Florian Kajuth; T. Knetsch; Nicolas Pinkwart,"Based on a stock-flow model of the housing market we estimate the relationship of house prices and explanatory macroeconomic variables in Germany using a regional panel dataset for 402 administrative districts. Using regional data exploits the variation across local housing markets and overcomes time-series data limitations. We take the regression residuals as a measure for deviations of actual house prices from their fundamental equilibrium level. The model specification allows to aggregate district-level residuals for various regional subsets. During the past two years for Germany as a whole single-family house prices appeared to be in line with their fundamental equilibrium level, whereas apartment prices significantly exceeded the fundamental price suggested by the model. The overvaluation of apartments is higher in towns and cities and most pronounced in the major seven cities, while single-family houses in cities appear to be only moderately above their fundamental levels.",23.0,https://doi.org/10.2139/ssrn.2796934 dff530280d1ac876dae29576e3aade69ac32526f,10.1016/j.jeconom.2021.05.003,Estimation and inference in heterogeneous spatial panels with a multifactor error structure,2021.0,,Jia Chen; Y. Shin; Chaowen Zheng,"Abstract We develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and common factors. To tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors, we propose the CCEX-IV estimation procedure that approximates factors by the cross-section averages of regressors and deals with the spatial endogeneity using the internal instrumental variables. We develop the individual and Mean Group estimators, and establish their consistency and asymptotic normality. By contrast, the Pooled estimator is shown to be inconsistent in the presence of parameter heterogeneity. Monte Carlo simulations confirm that the finite sample performance of the proposed estimators is quite satisfactory. We demonstrate the usefulness of our approach with an application to the house price growth for Local Authority Districts in the UK over 1997Q1–2016Q4.",23.0,https://eprints.whiterose.ac.uk/id/eprint/174055/1/CSZ_OS.pdf b1153f986ed9b6dd6d1a956c6a84b72532b224de,10.1080/24694452.2022.2155606,"Making the City of Lakes: Whiteness, Nature, and Urban Development in Minneapolis",2023.0,Annals of the American Association of Geographers,Rebecca H. Walker; Hannah Ramer; Kate Derickson; B. Keeler,"Minneapolis has the twin distinctions of having one of the most highly rated park systems in the United States and some of the most pronounced racial disparities in wealth and homeownership. We argue that this coupling of urban nature and racial inequality was intentionally produced by the city’s real estate industry and local government. Drawing on Mapping Prejudice’s first complete metro-wide map of racial covenants—clauses in property deeds barring sale to anyone not considered white—we pair quantitative spatial analysis with archival research on turn-of-the century greening campaigns and local real estate practices. We use two developments, Nokomis Terrace and Walton Hills, as illustrative examples of the ways in which developers worked with civil society organizations and local government agencies to secure public investments in green amenities, including gardens and public parks, while blanketing their developments with racial covenants. To boost property values, developers paired “greenness” and legal guarantees of whiteness, engineering idealized nature while excluding racialized groups. The result was that 73 percent of park acreage added from 1910 to 1955, the period in w",23.0, 432afefbc6bfb35c639fa331e7e39265ac8d1775,10.1111/j.0016-7363.2007.00712.x,An Anisotropic Model for Spatial Processes,2007.0,,Minfeng Deng,"One of the key assumptions in spatial econometric modelling is that the spatial process is isotropic, which means that direction is irrelevant in the specification of the spatial structure. On one hand, this assumption largely reduces the complexity of the spatial models and facilitates estimation and interpretation; on the other hand, it appears rather restrictive and hard to justify in many empirical applications. In this paper a very general anisotropic spatial model, which allows for a high level of flexibility in the spatial structure, is proposed. This new model can be estimated using maximum likelihood and its asymptotic properties are well understood. When the model is applied to the well-known 1970 Boston housing prices data, it significantly outperforms the isotropic spatial lag model. It also provides interesting additional insights into the price determination process in the properties market.",23.0,https://figshare.com/articles/thesis/An_anisotropic_model_for_spatial_processes/14874264 407c36cbaf7f3a12fea4c97cc669460996ed12fe,10.5089/9781513525839.001.a001,Predicting Downside Risks to House Prices and Macro-Financial Stability,2020.0,IMF Working Papers,Andrea Deghi; Mitsuru Katagiri; Sohaib Shahid; N. Valckx,"This paper predicts downside risks to future real house price growth (house-prices-at-risk or HaR) in 32 advanced and emerging market economies. Through a macro-model and predictive quantile regressions, we show that current house price overvaluation, excessive credit growth, and tighter financial conditions jointly forecast higher house-prices-at-risk up to three years ahead. House-prices-at-risk help predict future growth at-risk and financial crises. We also investigate and propose policy solutions for preventing the identified risks. We find that overall, a tightening of macroprudential policy is the most effective at curbing downside risks to house prices, whereas a loosening of conventional monetary policy reduces downside risks only in advanced economies and only in the short-term.",23.0,https://www.elibrary.imf.org/doc/IMF001/28616-9781513525839/28616-9781513525839/Other_formats/Source_PDF/28616-9781513527765.pdf 77ba361f1375441f9d9b928967cfb0dbcd3e8432,10.23977/bgdds.2018.11001,Research on Ensemble Learning-based Housing Price Prediction Model,2018.0,,Buyang Cao; Bo-Wen Yang,"Housing price is influenced by multiple factors. The existing housing price forecasting model usually belongs to the so called single predictor model, whose prediction accuracy is not ideal and the over-fitting phenomenon often happens due to the data noise. To resolve these issues, this paper proposes an ensembe lerning-based housing price prediction model incorporating various predictors. To evaluate the effectiveness of the proposed model, extra trees, random forest, GBDT and XGB algorithms are selected for the benchmarks. The dataset used is the California housing price available over the web. The results demonstrate that the proposed method can improve the predicting accuracy and stability compared with other four single prediction models.",23.0,http://www.clausiuspress.com/assets/default/article/2018/03/08/article_1520536624.pdf 5fb290e0d92ec4d051372a986ac309dc2e666bd6,10.1108/pm-11-2022-0086,Composite property price index forecasting with neural networks,2023.0,Property Management,Xiaojie Xu; Yun Zhang,"PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.",23.0, b4d90ac556c12775841d148b92b32c2089b062dd,10.68479/001c.9345,The Effects of Landfills on Rural Residential Property Values: Some Empirical Evidence,2000.0,Journal of Regional Analysis & Policy,R. Bouvier; J. M. Halstead; K. Conway; A. Manalo,"The question of whether solid waste landfills affect residential property values has long been a subject of debate. Past research has resulted in mixed conclusions. The current study examines six landfills, which differ in size, operating status, and history of contamination. The effect of each landfill is estimated by the use of multiple regression. In five of the landfills, no statistically significant evidence of an effect was found. In the remaining case, evidence of an effect was found, indicating that houses in close proximity to this landfill suffered an average loss of about six percent in value.",23.0,https://jrap.scholasticahq.com/article/9345.pdf aabfbb500e4203cdd6f3b6441bde32e72de639c4,10.1016/j.jhe.2018.06.008,Intra-school district capitalization of property tax rates,2018.0,Journal of Housing Economics,Mitchell R. Livy,"Abstract While previous studies have attempted to measure property tax rate capitalizations by proximate housing prices, disentangling school quality, and its associated amenities, from tax rates has impeded accurate estimation. I overcome this issue by investigating school districts containing varying tax rates. Identification is derived from the use of school and spatial-temporal boundary fixed effects to eliminate unobserved variation associated with tax districts that can confound capitalization estimates. Results from the primary model provide evidence of a negative capitalization of tax rate levels that corresponds to full capitalization at discount rate of nearly 3.5 percent. Further, property fixed effects are uniquely incorporated within the boundary identification method to show that tax rate changes are capitalized similar to baseline tax rate differences.",23.0, 934c228700b544511e5b1406a0a562934dbcfe58,10.14569/ijacsa.2021.0121291,Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur,2021.0,International Journal of Advanced Computer Science and Applications,Shuzlina Abdul-Rahman; Nor Hamizah Zulkifley; Ismail Ibrahim; S. Mutalib,,23.0,http://thesai.org/Downloads/Volume12No12/Paper_91-Advanced_Machine_Learning_Algorithms.pdf ab844b97c7c829c979609856376aac3442ae1d63,10.1016/j.habitatint.2022.102712,Analyzing housing price determinants in Izmir using spatial models,2022.0,Habitat International,Z. Sayın; Zeynep Elburz; H. Duran,,23.0,https://hdl.handle.net/11147/12649 d309e0674e949172ac323df2983c2c7b28535a37,10.1108/17538270910939583,The influence of submarkets on water view house price premiums in New Zealand,2009.0,,O. Filippova,,23.0, 6208c8b0b59e84812c99223c935c8973b5134cdd,10.1016/j.qref.2013.07.001,Estimation of variance of housing prices using spatial conditional heteroskedasticity (SARCH) model with an application to Boston housing price data,2014.0,,Prodosh E. Simlai,,23.0, 511e598b2593fba7c92993a7690d43b3b9b1df7d,10.1155/2020/1079024,"GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China",2020.0,Complex,Shao-Pei Chen; Da-Chang Zhuang; Huixia Zhang,"In the past decades, the booming growth of housing markets in China triggers the urgent need to explore how the rapid urban spatial expansion, large-scale urban infrastructural development, and fast-changing urban planning determine the housing price changes and spatial differentiation. It is of great significance to promote the existing governing policy and mechanism of housing market and the reform of real-estate system. At the level of city, an empirical analysis is implemented with the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation models, focusing in examining and characterizing the spatial homogeneity and nonstationarity of housing prices in Guangzhou, China. There are 141 neigborhoods in Guangzhou identified as the independent individuals (named as area units), and their values of the average annual housing prices (AAHP) in (2009–2015) are clarified as the dependent variables in regressing analysis models used in this paper. Simultaneously, the factors including geographical location, transportation accessibility, commercial service intensity, and public service intensity are identified as independent variables in the context of u",22.0,https://downloads.hindawi.com/journals/complexity/2020/1079024.pdf e52d7a2beea1503d75d99f66189fa08e2f2699ff,10.3390/land12122095,Assessing the Effects of Subjective and Objective Measures on Housing Prices with Street View Imagery: A Case Study of Suzhou,2023.0,Land,Jin Zhu; Yao Gong; Changchang Liu; Jinglong Du; Ci Song; Jie Chen; T. Pei,"The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (",22.0,https://www.mdpi.com/2073-445X/12/12/2095/pdf?version=1700642561 ca4fb2ebb95cdf0f90c126bff164537a2b769e09,10.1017/s1351324923000360,Describe the house and I will tell you the price: House price prediction with textual description data,2023.0,Natural Language Engineering,Han Zhang; Yansong Li; Paula Branco,"Abstract House price prediction is an important problem that could benefit home buyers and sellers. Traditional models for house price prediction use numerical attributes such as the number of rooms but disregard the house description text. The recent developments in text processing suggest these can be valuable attributes, which motivated us to use house descriptions. This paper focuses on the house asking/advertising price and studies the impact of using house description texts to predict the final house price. To achieve this, we collected a large and diverse set of attributes on house postings, including the house advertising price. Then, we compare the performance of three scenarios: using only the house description, only numeric attributes, or both. We processed the description text through three word embedding techniques: TF-IDF, Word2Vec, and BERT. Four regression algorithms are trained using only textual data, non-textual data, or both. Our results show that by using exclusively the description data with Word2Vec and a Deep Learning model, we can achieve good performance. However, the best overall performance is obtained when using both textual and non-textual features. An",22.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/807584C6B9D76F555DA61E220BF39F9B/S1351324923000360a.pdf/div-class-title-describe-the-house-and-i-will-tell-you-the-price-house-price-prediction-with-textual-description-data-div.pdf ca41667d6376df2dd421960a8fba5b3472e0ba45,10.1016/j.amepre.2014.08.025,Worksite Neighborhood and Obesogenic Behaviors: Findings Among Employees in the Promoting Activity and Changes in Eating (PACE) Trial,2014.0,American Journal of Preventive Medicine,Wendy E. Barrington; S. Beresford; T. Koepsell; G. Duncan; A. Moudon,"Background Understanding mechanisms linking neighborhood context to health behaviors may provide targets for increasing lifestyle intervention effectiveness. Although associations between home neighborhood and obesogenic behaviors have been studied, less is known about the role of worksite neighborhood. Purpose To evaluate associations between worksite neighborhood context at baseline (2006) and change in obesogenic behaviors of adult employees at follow-up (2007–2009) in a worksite randomized trial to prevent weight gain. Methods Worksite property values were used as an indicator of worksite neighborhood socioeconomic status (NSES). Worksite neighborhood built environment attributes associated with walkability were evaluated as explanatory factors in relationships among worksite NSES, diet, and physical activity behaviors of employees. Behavioral data were collected at baseline (2005–2007) and follow-up (2007–2009). Multilevel linear and logistic models were constructed adjusting for covariates and accounting for clustering within worksites. Product-of-coefficients methods were used to assess mediation. Analyses were performed after study completion (2011–2012). Results Higher wor",22.0,https://www.ncbi.nlm.nih.gov/pmc/articles/4418796 041ef096263bbb1506e1c6840cdc8a8528cb3d7a,10.1007/s44196-023-00216-7,An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy,2023.0,International Journal of Computational Intelligence Systems,Miao-Miao Liu; Yu-Ying Zhang; Jingfeng Guo; Jing Chen; Zhi-Gang Liu,"This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first, an adaptive factor is introduced to improve tent chaotic mapping and used for population position initialization to enhance population diversity and realize uniform traversal while ensuring random distribution, ultimately improving the global search ability. Second, to address the problem that the cub selection strategy is blind, resulting in insufficient traversal in the early stage, a dynamic step-size perturbation factor is established using the second-order norm and information entropy. Adaptive parameters are used to dynamically adjust the selection probability of different cub behaviors based on the number of iterations to suppress the premature convergence of the algorithm. Finally, tent chaotic search is employed to adaptively adjust the search range and improve the individuals with poor fitness through multiple neighborhood points of the local optimal solution, further improving the alg",22.0,https://link.springer.com/content/pdf/10.1007/s44196-023-00216-7.pdf 242219c9cc037b48afab875843fed5ceb5776f06,10.2478/mgr-2013-0011,"Using Multi-Scale Spatial and Statistical Analysis to Assess the Effects of Brownfield Redevelopment on Surrounding Residential Property Values in Milwaukee County, USA",2013.0,,Wen-Jie Sun; B. Jones,"Abstract Brownfield redevelopment has gained support in the U.S. as an essential ingredient of urban revitalization. Assessing the effects of such projects is important as government budgets tighten recently. Through multi-scale spatial and statistical analysis, this study shows the spatial patterns of residential property values and their changes, and investigates linkages to the presence of different types and sizes of nearby brownfield redevelopment projects, as opposed to neighborhood demographics and property characteristics. While the results of this study suggest brownfield redevelopment does play a positive role on the surrounding residential property values in general, there are quite different statistical significances found at the two levels of analysis and the type of redevelopment found to determine the direction of this effect. Shrnutí Revitalizace brownfields získala v USA podporu jako podstatná součást revitalizace měst. Hodnocení efektů takovýchto projektů je důležité, neboť státní rozpočty se v poslední době stále snižují. Tato studie uvádí pomocí vícerozměrné prostorové a statistické analýzy prostorové změny cen residenčních nemovitostí a studuje jejich závislost",22.0,https://sciendo.com/pdf/10.2478/mgr-2013-0011 78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,10.3390/buildings11050213,Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy),2021.0,Buildings,P. Morano; F. Tajani; F. Di Liddo; Michele Darò,"Among environmental factors, noise represents one of the most relevant determinants on human health and on the urban quality level and, consequently, on real estate values. Thus, the noise pollution issue plays a significant role in public urban policies aimed at increasing the acoustic comfort level and creating more sustainable and comfortable cities. The real estate market is highly sensitive to noise factor and the residential prices can be strongly influenced by a high acoustic pollution rate. The present research aims to analyze the functional relationships between noise pollution and selling prices in four municipal areas of the city of Bari (Southern Italy). For each area, a study sample constituted by two hundred residential properties sold in 2017–2019 was detected for the identification of the main influential factors on prices and the investigation of the contribution of noise on them. The implementation of an econometric technique was used to obtain four different models (one for each municipal area of the city of Bari) able to explain the specific impact of noise pollution level on selling prices. From the comparison of the results obtained for each area, the outputs ",22.0,https://www.mdpi.com/2075-5309/11/5/213/pdf?version=1621508083 e586d2d5da30c7b039a0e200f6460a3ba1f014a0,10.1016/j.jsr.2020.04.011,Spatial relationships between alcohol outlet densities and drunk driving crashes: An empirical study of Tianjin in China.,2020.0,Journal of Safety Research,Shao-Hua Wang; Yanyan Chen; Jian-Ling Huang; Zhuo Liu; Jia Li; Jian-Ming Ma,"INTRODUCTION Numerous studies have demonstrated the close relationship between alcohol availability and alcohol-related crashes. However, there is still a lack of spatial empirical analysis regarding this relationship, particularly in large cities of developing countries. Differences in alcohol outlets and drinking patterns in these cities may lead to quite different patterns of crash outcomes. METHOD 3356 alcohol-related crashes were collected from the blood-alcohol test report of a forensic institution in Tianjin, China. Density of alcohol outlets such as retail locations, entertainment venues, restaurants, hotels, and companies were extracted based on 2114 Traffic Analysis Zones (TAZ) together with the residential and demographic characteristics. After applying the exploratory spatial data analysis, this research developed and compared the traditional Ordinary Least Square model (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM) to explore spatial effects of all the variables. RESULTS The results of incremental spatial autocorrelation show that the most significant distance threshold of alcohol-related roadway traffic crashes is 3 km. Th",22.0, e80cdda8f965018e6660b46aac8860f14decc998,10.1080/09599916.2021.2023610,"The impact of COVID-19 on house prices in Northern Ireland: price persistence, yet divergent?",2022.0,Journal of Property Research,M. McCord; D. Lo; J. McCord; P. Davis; M. Haran; P. Turley,"ABSTRACT The recent onset of the COVID-19 pandemic has had a pervasive impact on all economies and indeed housing markets. This research investigates the regional impact of the pandemic on the Northern Ireland housing market and provides a unique opportunity to measure short-term reactions to epidemic shocks. Applying a unique dataset, the research measures whether price switching effects are evident as a consequence of the epidemic, and to what extent. In order to achieve this, the research applies spatial lag models to account for the effect of COVID-19 on housing market pricing behaviour. The findings show that the autocorrelation of house prices increased after COVID-19, revealing price persistence driven by behavioural changes. The results further show that a price divergent effect is observable, with the detached sector ‘leading’ the price changes. This price divergence is also apparent for rural dwellings and for neighbourhoods with higher socio-economic standing making them more resistant to the outbreak of COVID-19. This is an important finding as it reveals that epidemics of this nature impact upon housing markets in a heterogeneous way in the short-term, with a clear pre",22.0,https://www.tandfonline.com/doi/pdf/10.1080/09599916.2021.2023610?needAccess=true&role=button b544ee2cce99065be4e156dbd7d09e16e1093c53,10.3846/ijspm.2022.16255,SPATIAL EFFECT OF MARKET SENTIMENT ON HOUSING PRICE: EVIDENCE FROM SOCIAL MEDIA DATA IN CHINA,2022.0,International Journal of Strategic Property Management,Junjie Li; Yu Wang; Chunlu Liu,"Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide refe",22.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/16255/10976 26fdab97ef449613d13031877c858acaa34b423b,10.2139/ssrn.1673625,New Zealand Housing Markets: Just a Bit-Player in the A-League?,2010.0,,A. Grimes; Mark J. Holmes; Nicholas Tarrant,"House price trends in each of New Zealand and Australia are frequently discussed as national level developments. Sub-national developments are also important, especially where regions display idiosyncratic trends driven either by demand factors (differential income patterns) or by supply factors (geographical or regulatory restrictions). At a broader scale, it is possible that the New Zealand housing market, or a specific regional housing market (e.g. Auckland), is part of a broader Australasian housing market. If this were the case, New Zealand house prices would converge to a broadly stable ratio of house prices in Australia. One reason this could occur is if international macroeconomic and asset price trends dominate housing market outcomes. New Zealand authorities may then be relatively powerless to control the major real determinants of house prices through regulatory or other policies. We extract the major drivers of house prices at regional levels within New Zealand and Australia to examine the degree of differentiation across regional housing markets. While some minor regional differences are apparent, the evidence points to the dominance of a single trans-Tasman housing tr",22.0,http://ageconsearch.umn.edu/record/292616 ec44cf6549ef494bce3d9eb01368098f86be6c36,10.17016/feds.2017.086,The Effect of Interest Rates on Home Buying: Evidence from a Discontinuity in Mortgage Insurance Premiums,2017.0,,Neal Bhutta; D. Ringo,"We study the effect of interest rates on the housing market by taking advantage of a sudden and unexpected price change in a large government mortgage program. The Federal Housing Administration (FHA) insures most mortgages to lower-downpayment, lower credit score borrowers, including a majority of first-time homebuyers. The FHA charges borrowers an annual mortgage insurance premium (MIP), and in January, 2015 the FHA abruptly reduced the MIP, and thus FHA borrowers’ effective interest rate, by 50 basis points. Using a regression discontinuity design, we find that the MIP reduction increased the number of home purchase originations among the FHA-reliant population by nearly 14 percent. The response to the premium cut was negatively correlated with borrower income, with no observable response among relatively high income borrowers. We trace part of the jump in home buying to the MIP reduction helping ease binding debt payment-to-income ratio limits thus allowing more applications to be approved. Finally, we find no evidence that the MIP reduction increased house prices.",22.0,https://doi.org/10.17016/feds.2017.086 917c619016df37287467cf4e11b25adbb77eec99,10.1108/ijhma-05-2021-0058,The impact of COVID-19 on home value in major Texas cities,2021.0,International Journal of Housing Markets and Analysis,Mira R. Bhat; J. Jiao; A. Azimian," Purpose This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects. Design/methodology/approach This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic. Findings The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature. Originality/value Previous literature analyzed the housing market within the first phase of COV",22.0, fad3b3b12cd59030f766d7860e7dd8a2927d41da,10.1016/j.ceqi.2020.12.001,Winning at the starting line: The primary school premium and housing prices in Beijing,2021.0,China Economic Quarterly International,Xuan Han; Yan Shen; Bo Zhao,"Abstract Using housing transaction data from Beijing during 2013–16, this paper evaluates the education premium for good primary schools and its changes. We use the regression discontinuity method to estimate the average education premium and its variation year by year. We take advantage of differences in the right to school enrollment between homeowners and renters to determine the discontinuity boundaries. Furthermore, the difference-in-differences method is applied to investigate the changes in housing prices triggered by changes in the school attendance zones. We find that the average education premium for the top 59 high-quality primary schools in Beijing was about 11% and increased each year. During the sample period, the increase accumulated to more than 50%. The changes in the school attendance zones led to a significant 1.5%–3.5% rise in housing prices. This shows that it would be better to increase the supply of quality education rather than only guiding the demand for it to promote educational equity.",22.0,https://www.sciencedirect.com/science/article/pii/S2666933121000022/pdf fc2b33f9da455b9087ed4e52a292699a7b3b4361,10.2478/remav-2019-0036,House Price Convergence on the Primary and Secondary Markets: Evidence from Polish Provincial Capitals,2019.0,,Mateusz Tomal,"Abstract The aim of this study is to identify whether there is a common house price trend across provincial capitals in Poland. The log t regression is the main method of analysis. Additionally, traditional convergence tests based on the concepts of β- and σ-convergence are used. The obtained results indicate that the cities do not share a common price in the long-run. There are, however, convergence clubs on both primary and secondary markets. In each club, house prices across cities tend to converge to their own steady state. Moreover, research on the driving forces of convergence reports that factors affecting housing prices differ among the clubs. Therefore, policymakers should adjust housing policies in accordance with the characteristics of a given club. In turn, the σ-convergence model demonstrated a very interesting finding, namely, a U-shape pattern of convergence, both on the primary and secondary markets. This pattern is strictly correlated with the level of prices on the markets.",22.0,https://doi.org/10.2478/remav-2019-0036 9f5c475bea83130e393c68d8dc5f886a776e0f3e,10.1016/j.jhe.2020.101731,Heterogeneity in households’ expectations of housing prices – evidence from micro data,2020.0,,Erik Hjalmarsson; P. Österholm,"Expectations about future housing prices are arguably an important determinant of actual housing prices, and an important input in decisions on whether and how to transact in the housing market. Using novel micro-level survey data on Swedish households, we analyse households’ expectations of housing prices and how these expectations relate to the characteristics of the respondents. Results show that age is significantly related to housing-price expectations, with the youngest households – whose adulthood largely corresponds to the extended period of rapid housing-price growth in Sweden – having the highest housing-price expectations, thus lending support to the hypothesis that expectations are influenced by personal experiences. Our findings suggest that aggregate measures of expectations might hide important features of the data, which could be of interest to policy makers when choosing regulatory actions or formulating macroprudential policies.",22.0,https://www.sciencedirect.com/science/article/pii/S105113772030067X/pdf 6c0fa6707be0f8c5edd0d4088f7dfb356412099f,10.1017/s1355770x18000050,"The demand for air quality: evidence from the housing market in Bogotá, Colombia",2018.0,Environment and Development Economics,Fernando Carriazo; John Gomez-Mahecha,"Abstract Identifying a demand function for air quality is a key input to calculate welfare measurements of pollution abatement policies. We defined intra-urban housing submarkets to apply a Second Stage hedonic pricing model that allowed us to identify an inverse demand function for PM10 reductions in Bogotá. The monthly benefits of compliance with the U.S. Environmental Protection Agency standard (50 μg/m3 – annual average), and the far more stringent World Health Organization standard (20 μg/m3 – annual average) are valued at US$12.16 and US$189.64 per household, respectively. These values, in turn, represent about 1.35 per cent and 21.04 per cent of the average household income. The hedonic model applied hereby shows that intra-urban housing submarkets are suitable for the identification of a demand function to be used by policy makers interested in evaluating non-marginal benefits (costs) from air quality improvements (deterioration).",22.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/82E15EF74DA4284EFEFE3F9A93B94FA3/S1355770X18000050a.pdf/div-class-title-the-demand-for-air-quality-evidence-from-the-housing-market-in-bogota-colombia-div.pdf 777957c0090cd00b170d5f05a88e42c6b9fe0f94,10.1080/02673037.2019.1614537,Inequalities in the transition to homeownership among young people in Scotland,2019.0,Housing Studies,F. Fiori; E. Graham; Zhi-Qiang Feng,"Abstract Homeownership levels among young adults in the UK are declining. This paper compares youth transitions to homeownership in Scotland during the 1990s and 2000s by examining the roles of both personal and parental socio-economic characteristics and local house prices. It demonstrates demographic diversity among young people, with gender and partnership status interacting to shape their transitions to homeownership. The findings reveal that, although single women are less likely than single men to become homeowners, women are more likely to make the transition if they live with a partner. For all young adults, patterns of advantage and disadvantage are defined by personal resources and parental background. While many of these inequalities have persisted over time, the distance between the most and least advantaged has widened, new inequalities have emerged and local housing markets have come to play a greater role for some.",22.0,https://www.tandfonline.com/doi/pdf/10.1080/02673037.2019.1614537?needAccess=true 627848628da8709abcbb84a4ef7502a61f755539,10.3846/ijspm.2020.12742,CAN MACHINE LEARNING ALGORITHMS ASSOCIATED WITH TEXT MINING FROM INTERNET DATA IMPROVE HOUSING PRICE PREDICTION PERFORMANCE?,2020.0,International Journal of Strategic Property Management,Jian-qiang Guo; Shu-hen Chiang; Min Liu; Chien-Chun Yang; Kai Guo,"Housing frenzies in China have attracted widespread global attention over the past few years, but the key is how to more accurately forecast housing prices in order to establish an effective real estate policy. Based on the ubiquitousness and immediacy of Internet data, this research adopts a broader version of text mining to search for keywords in relation to housing prices and then evaluates the predictive abilities using machine learning algorithms. Our findings indicate that this new method, especially random forest, not only detects turning points, but also offers prediction ability that clearly outperforms traditional regression analysis. Overall, the prediction based on online search data through a machine learning mechanism helps us better understand the trends of house prices in China.",22.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/12742/9995 24ec981dfb3f074373a1774c3995629adcbe1628,10.1016/j.cities.2019.102444,Spatial dependence of housing values in Northeastern Italy,2020.0,,Sergio Copiello,"Abstract This study aims to test whether spatiotemporal dependence exists in residential property values in Northeastern Italy, or it is insignificant once accounting for exogenous explanations. A peculiar feature is that the analysis focuses on aggregate housing values, at the level of municipalities, rather than individual housing units. Based on the literature on spatial data analysis, a general serial and spatial model is used. Along with several demographic and socioeconomic predictors, two time lags and two spatial lags of the dependent variable are considered. As far as the results are concerned, serial dependence is more important than spatial dependence. Moreover, there are clues that the exogenous explanations cause the spatial effects to fade away, which would support the claim of the skeptics on the occurrence of spatial spillovers, namely, that peer and neighborhood effects are mostly a matter of neglected, underlying influences. However, some spatial dependence is intrinsic in the analyzed data and failing to consider it may lead to biased estimates. The results provide a deeper understanding of how housing values possibly spread across space. They are also suitable f",22.0, ce263f41bf2280c0b7021c66e88a17ddcc6d4fb8,10.1109/indin45582.2020.9442244,Housing prices prediction with deep learning: an application for the real estate market in Taiwan,2020.0,International Conference on Industrial Informatics,Choujun Zhan; Zeqiong Wu; Yonglin Liu; Zefeng Xie; Wan-I Chen,"The housing market is increasing huge, predicting housing prices is not only important for a business issue, but also for people. However, housing price fluctuations have a lot of influencing factors. Also, there is a non-linear relationship between housing prices and housing factors. Most econometric or statistical models cannot capture non-linear relationships yet. Therefore, we propose housing price prediction models based on deep learning methods, which can capture non-linear relationships. In this work, we construct a dataset, including the housing attributes data and macroeconomic data in Taiwan from January 2013 to December 2018. The housing attributes data includes two types of housing transactions, which are “land + building” (Type1) and “land + building + park” (Type2). Macroeconomic data includes housing investment demand ratio, owner-occupier housing ratio, housing price to income ratio, housing loan burden ratio, and housing bargaining space ratio. Then, this dataset is utilized to evaluate the prediction methods based on deep learning algorithms BPNN and CNN to predict housing prices. Experimental results show that CNN with housing features has the best prediction eff",22.0, fc529dd32d2d43a40e1cb31c84ae6c319b2c910c,10.1016/j.scitotenv.2023.164603,Climate gentrification along with parks' cooling performance in one of China's tropical industrial cities.,2023.0,Science of the Total Environment,Manqing Shi; Yu-Tong Wang; Hangquan Lv; Wenxiao Jia,"Industrialization processes have promoted economic development at the cost of climate change and heat hazards. Urban parks are effective nature-based cooling strategies, but may also bring about climate gentrification. Our study explored climate gentrification along with park cooling performance based on land surface temperature retrieved from satellite images and housing prices in Liuzhou, one of China's tropical industrial cities. We found that urban parks were with average cooling distance of 166.17 ± 11.69 m, cooling intensity of 2.85 ± 0.28 °C, covering 41.06 ± 6.98 ha, about five times park area. The cooling lapse was 3.97 ± 0.40 °C/km. Different accessibility to park cooling area led to climate gentrification. Residents in the urban center had better access to park cooling range than those outside the second ring road. Housing prices increased with decreasing distance to cooling range of urban parks. Measures should be taken to abate climate gentrification, such as improving parks' cooling performance and building affordable houses. This study has significant implications for quality, efficiency and equity of park construction, and provides suggestions for urban heat mitigat",22.0, eaa887dce0cb1525f29d159e88679f9d7c2b2894,10.1016/j.enbuild.2019.03.036,Prediction and analysis of heating energy demand for detached houses by computer vision,2019.0,Energy and Buildings,Miroslav Despotovic; David Koch; Sascha Leiber; M. Döller; M. Sakeena; M. Zeppelzauer,"Abstract Exterior images of real estate contain a large number of visual clues which allow conclusions about the heating energy demand (HED) of a building. Up to now, HED has been determined by specially trained experts such as architects, civil engineers, etc. either on the basis of consumption data or estimated demand values. In this article, we present a novel approach to determine the HED of detached houses. Our suggested approach is based solely on the visual appearance and assumes that exterior images of a building contain a variety of information that allows inferences about the HED of a building. For this, we use the powerful techniques of image analysis and computer vision which are already successfully used in different domains like surveillance, image search, and robotics. The results show that our approach works well and in addition to the HED, the construction period of a building can also be determined. Our algorithm achieves a classification accuracy of 62% for HED and 57% for construction age epoch.",22.0, 97d464f5865665163d982da69f7dabef3299b19c,10.1177/0042098020972636,Burglaries and entry controls in gated communities,2021.0,Urban studies,Zengli Wang; Lin Liu; Cory P. Haberman; Min-Xuan Lan; Bo-Xiong Yang; Han-Lin Zhou,"This article examines whether different levels of entry controls impact burglary rates in gated communities. It differs from the previous studies that only distinguish gated communities from non-gated communities but ignore important variation in different levels of entry controls. A sample of 698 gated communities in a large Chinese city are selected for this study. A negative binomial regression model estimates the relationships between entry control levels and burglary rates in gated communities. The test of these relationships accounts for the control of other important explanatory variables, including management fee, building height, building age, housing price, house for sale, rental house and floating population. Results indicate that higher entry control levels are associated with significantly lower burglary rates in gated communities. This is the first study that reveals a quantitative relationship between burglary and entry control level in gated communities at the city-wide scale.",22.0, a89208d943bf56995edc3e1f15a194051dbdf01e,10.1080/19376812.2020.1761844,Examination of the dynamics of house prices in urban Ghana,2020.0,,Anthony Owusu-Ansah; Wilfred Anim-Odame; S. Azasu,"ABSTRACT The paper examines the main macroeconomic determinants of house price movements in Accra, the capital of Ghana using the VECM. The results show that the main drivers of price movements in the housing market in Accra are GDP growth, unemployment and interest rates, and there is no long-term relationship between these significant variables and house prices in the country. Any short-term disequilibrium is not able to correct itself over the long-term; hence the fundamentals do not explain the price changes in the long run. The study, which is a novelty in Ghana has produced results with policy implications in dealing with housing affordability and promoting residential market in general.",22.0, 845531ab7d4134ec494bacc723e3671381ac83c1,10.1016/j.cities.2024.105661,Evaluation of green space influence on housing prices using machine learning and urban visual intelligence,2025.0,Cities,Yan-Qing Xu; Ruidun Chen; Hong-Yu Du; Meixu Chen; Cong Fu; Yuchen Li,,22.0,https://doi.org/10.1016/j.cities.2024.105661 ac29d2d96e15613eb81fdbe04f9912aebb524004,10.1108/jfmpc-08-2019-0068,An exploratory investigation into the relationship between energy performance certificates and sales price: a polytomous universal model approach,2020.0,Journal of Financial Management of Property and Construction,M. McCord; P. Davis; John McCord; M. Haran; K. Davison,"PurposeThe role of energy efficiency and particularly energy performance certificates (EPCs) has emerged as a topical and important aspect of real estate markets. Various studies have been carried out investigating the perceived capitalisation effects of energy efficiency on property prices. There, however, remains divergence of opinion whether the capitalisation effect is truly in existence with extant research showing differing magnitudes of effects, if any. To date, no study (that the authors are aware of) has investigated the nature of the transition between EPC bands and price effects. The purpose of this study is to add to the research of the energy efficiency of housing to examine the nature of the likelihood of property characteristics being associated with higher EPC scores and value.Design/methodology/approachThis research undertakes a suite of methodological tests to investigate the more latent relationships between EPC bands and pricing behaviour using 3,797 achieved sales prices within the Belfast housing market. Binary logit regression models are specified in conjunction with a Polytomous Universal Model to examine the likelihood of EPC bands falling within a particul",21.0,https://pure.ulster.ac.uk/en/publications/42fba68f-0ca3-497f-999d-4398a21c3d87 2500a10eca56dc69f0c8e84c57bb76f725cf77b0,10.3390/su11092627,"Perspectives of Socio-Spatial Differentiation from Soaring Housing Prices: A Case Study in Nanjing, China",2019.0,Sustainability,Chun-Hui Liu; Wei-Xuan Song,"Launched in 1998, the market-oriented reform of urban housing has given urban housing the dual attributes of residence and investment, and led to the rapid growth of housing prices as well as the intensification of its spatial differentiation within cities. However, the spatial patterns of the differentiation and its mechanism as well as socio-spatial effects are rarely touched. This paper studies 3963 urban residential districts in central Nanjing and explores the socio-spatial differentiation pattern and process of the urban housing prices and its growth in Nanjing based on the sample data of housing transactions over 30 quarters during the period of 2009–2017. The paper concludes that, by splitting the research duration into phases of six quarters each, the average housing prices in Nanjing alternates between “rapid growth” and “relatively stable” phases. At the same time, this paper finds that the spatial heterogeneity of housing prices in the city has been enhanced constantly, and the price gap in different types of residential housing has been clearly widened. In combination with the price level, location characteristics and architectural attributes of residential districts, ",21.0,https://www.mdpi.com/2071-1050/11/9/2627/pdf?version=1557226234 d3a050a61c85805dd91681d85137d25f94b5f00a,10.1016/j.habitatint.2020.102265,Identification and analysis of urban Shrinkage risk based on residential property values,2020.0,,Xiao-Nan Qin; Wenhua Yuan; Jianchun Li; Xiaoxing Qi,"Abstract With the adjustment of the economic structures and the slowdown of development, urban shrinkage has become a common phenomenon, especially in developed countries. In contrast, for developing cities, there is a special phenomenon of urban shrinkage, that is, border expansion and space shrinkage coexist. In view of this situation, it is difficult to effectively identify the signs and trends of urban degradation and space shrinkage by only studying the macro scale of urban space. The purpose of this paper is to build an evaluation model of urban space shrinkage risk, which can be run for the whole process of urban space development. It can also be applied to diverse scales of urban space and identify the mechanism of the transformation and diffusion of the space shrinkage risk on the time scale and the space scale. This study took Jinan City in China, a typical second-tier city, as a sample of urban shrinkage and took the urban residential property values as the key index to evaluate the shrinkage risk. The results show the risk of urban space shrinkage on the micro-scale. This paper analyzes the transfer and spillover effects of the urban space shrinkage risk by using Moran'",21.0, 88ae15608aafe50fd455ec5983b48e899baff9fc,10.1080/24694452.2017.1310021,"Using Internal Migration to Estimate the Causal Effect of Neighborhood Socioeconomic Context on Health: A Longitudinal Analysis, England, 1995–2008",2017.0,,M. Green; Mariana C. Arcaya; Sankaran Subramanian,"There is long-standing evidence for the existence of geographical inequalities in health. Multiple conceptual frameworks have been proposed to explain why such patterns persist. The methodological design for these studies is often not appropriate for identifying causal effects of neighborhood context, however. It is possible that findings that show the importance of neighborhoods could be subject to confounding of individual-level factors, neighborhood sorting effects (i.e., health-selective migration), or both. We present an approach to investigating neighborhood-level factors that provides a stronger examination for causal effects, as well as addressing issues of confounding and sorting. We use individual-level data from the British Household Panel Survey (1995–2008). Individuals were grouped into quintiles based on the median house price of an individual's lower super output area as our measure of neighborhood socioeconomic context. Multivariate propensity scores were used to match individuals to control for confounding factors, and logistic regression models were used to estimate the association between destination of migration and risk of poor health (up to ten years following",21.0,http://hdl.handle.net/1721.1/117541 199a21c5e3757d0bb2c252c53df7c0b1083dc994,10.3390/land11112087,"Impact of Human Disturbances on the Spatial Heterogeneity of Landscape Fragmentation in Qilian Mountain National Park, China",2022.0,Land,Baifei Ren; Keunhyun Park; A. Shrestha; J. Yang; M. McHale; Wei-Lan Bai; Guangyu Wang,"Qilian Mountain National Park (QLMNP) is a biodiversity hotspot with great agriculture and tourism resources. With the expansion of human activities, a few areas of the park are experiencing massive landscape transformation, and these areas are also highly ecologically sensitive. Nevertheless, there are substantial differences in the human activities and natural resources of various communities around QLMNP, resulting in heterogeneous landscape degradation. Hence, this study explores the extent and drivers of spatial heterogeneity in landscape fragmentation associated with ecologically vulnerable communities in QLMNP. Multiple ring buffer analysis and geographically weighted regression (GWR) were used to analyze the relationships between landscape fragmentation and variables of human activities and facilities to identify the main factors influencing landscape fragmentation in different regions. The results reveal that human disturbance had a stronger relationship with landscape fragmentation in QLMNP than natural factors do. Among the drivers of landscape fragmentation, the distribution of residential areas and the extension of agricultural land were found to have more significant ",21.0,https://www.mdpi.com/2073-445X/11/11/2087/pdf?version=1669087696 6bf48d4a70326e414f2a02945bb5c5e78ef87c5b,10.5435/jaaos-d-22-00271,Orthopaedic Surgeon Distribution in the United States,2022.0,Journal of the American Academy of Orthopaedic Surgeons,Carlos D. Ortiz-Babilonia; Kevin C. Mo; Micheal Raad; J. Ficke; A. Jain,"Background: There is limited research on the supply and distribution of orthopaedic surgeons in the United States. The goal of this study was to analyze the association of orthopaedic surgeon distribution in the United States with geographic and sociodemographic factors. Methods: County-level data from the US Department of Health and Human Services Area Health Resources Files were used to determine the density of orthopaedic surgeons across the United States on a county level. Data were examined from 2000 to 2019 to analyze trends over time. Bivariate and multivariable negative binomial regression models were constructed to identify county-level sociodemographic factors associated with orthopaedic surgeon density. Results: In 2019, 51% of the counties in the United States did not have an orthopaedic surgeon. Metropolitan counties had a mean of 22 orthopaedic surgeons per 100,000 persons while nonmetropolitan and rural counties had a mean of 2 and 0.1 orthopaedic surgeons per 100,000 persons, respectively. Over the past 2 decades, there was a significant increase in the percentage of orthopaedic surgeons in metropolitan counties (77% in 2000 vs 93% in 2019, P < 0.001) and in the pro",21.0, 406039818c118556df2a3a1a0c98839c8c5791a6,10.3390/su12125010,Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data,2020.0,Sustainability,Peng-Fei Lin; Jian-Cheng Weng; Dimitrios Alivanistos; Si-Yong Ma; Bao-Cai Yin,"Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to their departure time using the latent Dirichlet allocation model. To enhance the interpretation of the behavior patterns in each cluster, we investigated the relationship between the socioeconomic characteristics of the residence locations and commuter cluster distributions. Approximately 3.1 million cardholders were identified as commuters, accounting for 67.39% of daily passenger volume. Their commuting routes indicated the existence of job–house imbalance and excess commuting in Beijing. We further segmented commuters into six clusters with different temporal patterns, including two-peak, staggered shifts, flexible departure time, and single-peak. The residences of commuters are mainly con",21.0,https://www.mdpi.com/2071-1050/12/12/5010/pdf?version=1593692332 cb799a0586d2b7515f5a1af05729329c0c009873,10.3390/rs12040608,"Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data",2020.0,Remote Sensing,Min Xu; C. Cao; Peng Jia,"Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolut",21.0,https://www.mdpi.com/2072-4292/12/4/608/pdf?version=1582521025 37576a1bae15a8f6d858393f06d7db50ffe187bc,10.5220/0011362000003440,Influencing Factors of Housing Price in New York-analysis: Based on Excel Multi-regression Model,2022.0,Proceedings of the International Conference on Big Data Economy and Digital Management,Yuqiao Yan,": Since the beginning of 21th century, the real estate market in New York has experienced rapid growth and the current housing price index is more than doubled from 2000. To figure out what can be related to the boom of the real estate market, this paper investigates the factors that influence the housing price index in New York. Specifically, the investigation focuses on data of recent 20 years (collected from ECONOMIC RESEARCH of FRED), using EXCEL to construct a linear regression model to find the correlation between the housing price index and four potential main factors-resident population, per capita personal income, GDP and home ownership rate. Also, the significance of each factor in the linear regression model is also considered and the improved model is based on eliminating non-significant factors. The result shows the housing price index has a strong correlation with GDP and home ownership rate. For this result, a further analysis based on macroeconomics cycle and housing bubble indicates that the housing price in New York may not correspond to the GDP growth in recession or recovery period. For home ownership rate, it can affect the housing price independently, but it i",21.0,https://doi.org/10.5220/0011362000003440 4583a34f4a4d20d25ffca78cdf9546e8bf10a7fb,10.3390/land11091600,The Spatio-Temporal Pattern and Spatial Effect of Installation of Lifts in Old Residential Buildings: Evidence from Hangzhou in China,2022.0,Land,Xin-Jun Dai; Zeling Li; Lindong Ma; Jing Jin,"In recent years, in order to improve the quality of living, China has carried out a series of urban renewal projects, such as adding elevators to old communities. Taking Hangzhou as an example, this paper studies the decision to install elevators in old residential buildings. This study used cold spots and hot spots, local autocorrelation and fishnetting analysis to show the spatial and temporal patterns of lift retrofitting in old communities. Finally, the spatial lag model is used to test whether there is a spatial spillover effect in the behavior of residents installing elevators. The research shows that: (1) Hot spots areas for retrofitting lifts are clustered in the central urban areas of Hangzhou, such as the intersection of Shangcheng District, Xiacheng District, Jianggan District, Gongshu District and Xihu District (scenic area); (2) The trend of retrofitting areas spreading from the center to the periphery; (3) In different communities, the number of elevators added in neighboring communities has a positive effect on the decision of adding elevators in other local communities. Therefore, the neighborhood effect cannot be ignored in the topic of community renovation, in whi",21.0,https://www.mdpi.com/2073-445X/11/9/1600/pdf?version=1663491503 4361b4480d2de6221e77caa91d8026b47c594a04,10.3390/su11113194,"Population Aging, Household Savings and Asset Prices: A Study Based on Urban Commercial Housing Prices",2019.0,Sustainability,Shi-Hong Zeng; Xinwei Zhang; Xiaowei R Wang; G. Zeng,"Currently, China’s aging population, high savings rate and high housing asset prices coexist, which has become a hot issue in academic research. First, considering the life-cycle hypothesis and overlapping generations model, asset prices are negatively correlated with the population dependency ratio and positively correlated with household savings. Second, based on census data from prefecture-level cities, a pooled regression model and two-stage least squares (2SLS) are used in this empirical research. The child dependency ratio was found to have a significant negative impact on housing prices, while the elderly dependency ratio had a positive impact on housing prices. The positive relationship between household savings and housing prices is highly significant. Finally, the interaction analysis shows that the impact of population aging on housing prices differs under different levels of household savings; thus, population aging affects housing prices through household savings, and the mediator dilutes and weakens this impact. The elderly generation’s release of savings could gradually inhibit housing prices. Population aging causes long-run downside risks but not a market meltdown.",21.0,https://www.mdpi.com/2071-1050/11/11/3194/pdf?version=1559897577 59b5ed19cb2ffcbedb32ba932b2f0e26b3dffd2c,10.11591/ijece.v13i4.pp4516-4525,The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market,2023.0,International Journal of Electrical and Computer Engineering (IJECE),Laith T. Khrais; Osman Shidwan,"The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy.",21.0,https://ijece.iaescore.com/index.php/IJECE/article/download/30809/16730 744649b4f02d97b30ff0e6ba7b373f746305e8b7,10.3390/s19143167,"Potentiality of Using Luojia1-01 Night-Time Light Imagery to Estimate Urban Community Housing Price—A Case Study in Wuhan, China",2019.0,Italian National Conference on Sensors,Chang Li; Linqing Zou; Yi-Jin Wu; Hui-Min Xu,"The first professional night-time light remote sensing satellite in China, Luojia1-01, has raised the resolution of night-time light data to 130 m, which provides a possibility for the study of small-scale night-time light. This paper is the first research on spatial analysis and quantitative modeling between night-time light intensity (NTLI) and community housing price (CHP) on a small scale by using the Luojia1-01 night-time light imagery. This paper takes Wuhan as the research area, CHP data obtained by web-crawler technology as the research object, combines Luojia1-01 data, and carries out spatial correlation analysis and quantitative modeling on a small scale for them. The experimental results show that there is a strong linear positive correlation between the NTLI and CHP based on geographically weighted regression (GWR), and the CHP data in Wuhan have obvious spatial non-stationarity. Moreover, the coupling mechanism between the NTLI and CHP is also revealed. We can conclude that there is potential for estimating the CHP by using Luojia1-01 night-time light imagery.",21.0,https://www.mdpi.com/1424-8220/19/14/3167/pdf 25e63a0f837c55d437d8c5968ae5db9d9e0bce3a,10.1016/j.jhe.2021.101768,Unexpected Housing Wealth Appreciation and Stock Market Participation,2021.0,Social Science Research Network,Dongmin Kong; Yawen Cheng; Li-Hua Liu,"Abstract This study examines the causal effects of housing wealth appreciation on the stock market participation of households in China. Households that purchase a house with an area less than or equal to 90 m2 faced a significantly lower down payment following the unexpected announcements of a series of policies beginning in 2006, thus creating discontinuity in house size and a housing price jump around the cutoff point. Using the policy shock to implement a regression discontinuity design, we find the following: (1) Unexpected housing wealth appreciation substantially promotes households’ stock market participation. (2) The housing wealth effect relaxes households’ liquidity constraints and thus encourages them to invest in stocks. (3) The housing wealth effect is pronounced among younger individuals and employees of state-owned enterprises. Our findings support the positive housing wealth effect on stock market participation and provide clear policy implications for regulators.",21.0,https://doi.org/10.2139/ssrn.3849025 531d6d0273e00a3bc037f290a1eed143097708f2,10.14419/ijet.v7i4.30.22378,Modelling House Price Using Ridge Regression and Lasso Regression,2018.0,International journal of engineering and technology,Sen Xin; K. Khalid,"House price prediction is important for the government, finance company, real estate sector and also the house owner.  The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of  house size, age of house, condition of house and also the location of the house.",21.0,https://www.sciencepubco.com/index.php/ijet/article/download/22378/10978 87ecd21a30ea9d1def62f8bb1254d9d3e4f11df2,10.18421/tem121-17,House Price Prediction Model Using Random Forest in Surabaya City,2023.0,TEM Journal,Rinabi Tanamal; Nathalia Minoque; Trianggoro Wiradinata; Y. Soekamto; T. Ratih,"A home is one of many fundamental human needs. Therefore, it is essential to arrange so that each family has a separate dwelling. Several prediction algorithms are presented in this study to forecast future property values. By interviewing real estate agents, combining many interviews with multiple agents engaged in the purchasing and selling of homes. Consequently, this study investigates Surabaya Real estate price forecasting models employing Random Forest machine learning algorithms and adopting seventeen regularly used characteristics from real estate agents, which are the most influential factor in determining house prices. The final model may assist in determining the appropriate price for the house. Several research trials have been conducted to achieve a high predictive value; however, the highest predictive value was achieved by using 80% of the data set for training and 20% of the data set for testing to provide output values with an 88% accuracy rate.",21.0,https://www.temjournal.com/content/121/TEMJournalFebruary2023_126_132.pdf 2b2550a1839b8cefb634863e2676314f054e57de,10.1080/13549839.2021.1886063,Hurricane Harvey: equal opportunity storm or disparate disaster?,2021.0,Local Environment : the International Journal of Justice and Sustainability,Katherine Lieberknecht; Deidre Zoll; J. Jiao; Katherine Castles,"ABSTRACT In the wake of Hurricane Harvey, multiple media sources claimed the event was an “equal opportunity” storm. These claims challenge existing social vulnerability research that finds marginalised groups have both unequal risk exposure and subsequent differential impacts from disasters (Cutter, Boruff, and Shirley 2003, “Assessing Current and Future Freshwater Flood Risk from North Atlantic Tropical Cyclones via Insurance Claims.” Scientific Reports 7 (February): 41609; Faber 2015, “Superstorm Sandy and the Demographics of Flood Risk in New York City.” Human Ecology 43 (3): 363–378. https://doi.org/10.1007/s10745-015-9757-x; Thomas et al. 2013, Social Vulnerability to Disasters. Boca Raton, FL: CRC Press). To understand the accuracy of this “equal opportunity” assertion, we use ordinary least squares, simultaneous autoregressive, and geographically weighted regression techniques to investigate the relationships between indicators of social vulnerability and FEMA-approved housing damages from Hurricane Harvey across five cities in the Houston area. Findings suggest Harvey had disproportionate impacts on housing damages for Black, Latinx, and mobile home residents. These result",21.0, 401c2aaf392cfea1b8314f2c2124d596adfc5d39,10.1109/conit55038.2022.9848309,Boston House Price Prediction Using Regression Models,2022.0,2022 2nd International Conference on Intelligent Technologies (CONIT),Saptarsi Sanyal; Saroj Kumar Biswas; Dolly Das; Manomita Chakraborty; Biswajit Purkayastha,"Everyone wishes to buy and live in a house which suits their lifestyle and which provides amenities according to their needs. There are many factors that are to be taken into consideration like area, location, view etc. for prediction of house price. It is very difficult to predict house price as it is constantly changing and quite often the prices are exaggerated for which people who want to buy houses, and various real estate agencies who want to invest in properties, find it difficult to buy or sell houses. For this reason, in this paper the author creates an advanced automated Machine Learning model using Simple Linear Regression, Polynomial Regression, Ridge Regression and Lasso Regression using the Boston house dataset to predict house price in future accurately, and to measure the accuracy of these models various measuring metrics like R-Squared, Root Mean Square Error (RMSE) and Cross-Validation are used. This paper also studies the correlation of various attributes of the Boston dataset using the heat map to see which attributes actually impact the prediction of the models. It removes the outliers which are present in the dataset to achieve good accuracy. In this paper it ",21.0, 5c2fb5e7ee3909f6946bb924b3d4de4f42de07a1,10.1111/grow.12407,"Rural land reforms and villagers' preferences for urban settlement: A case study of Shandong Province, China",2020.0,,Wei Yu; Xiao Lu; En-Ru Wang,"Based on surveys conducted in Shandong Province, China, this paper examined the interests of rural residents to move to and settle in cities, as well as the factors that motivate or discourage their potential settlement changes. Results of the binary logistic regression analysis showed that villagers' urban settlement preference was related to sociodemographic factors such as gender, age, and occupation. Overall, the separation of the “three rights” (collective ownership, land contract right, and land use right) associated with rural land has strengthened the urban settlement preference of rural residents. Nevertheless, we did not find significant association between land transfer and the preference for urban settlement. For villagers who expressed interest in urban settlement, better job opportunities, education opportunities, and higher income are the leading “pull” factors that would attract them to cities. The attachment to rural life, high living costs, and high housing prices in cities were the top concerns for villagers who did not want to move. The study also found that villagers' desired urban destinations changed with the changing land parameters, indicating that the outc",21.0, 9093751032a94b4c57eca580b33f82665fd0764b,10.1080/13549839.2019.1698529,"Multimodal travel behaviour, walkability indices, and social mobility: how neighbourhood walkability, income and household characteristics guide walking, biking & transit decisions",2020.0,,W. Riggs; S. Sethi,"ABSTRACT Recent research in city planning has measured “walkability” and multi-modalism through quantification of the built environment. Results have shown correlations with increased property values and neighbourhood stabilisation as well as a lack of spatial distribution of accessible resources based on socio-economic class. Some studies suggest that improvements in these measures can be made, particularly with regard to commute mode diversity. This study tests and scrutinises the association between a walkability index and multimodal travel behaviour – controlling for both built environment and socio-demographic attributes of agents in investigating stated non-vehicular transportation modes. It finds that walkability indexes are connected to multimodal behaviours broadly; whether it be those who walking, bicycling, and or taking transit. It also finds that distance is a significantly associated with transit behaviour in walkable neighbourhoods, but the probability of walking or cycling is tied more to the built environment factors, age, household income and size. These results suggest that cities might benefit from policy efforts that not only continue to address issues of spati",21.0, 504e66bdd395573601c08a21017bcec2eb92b623,10.52324/001c.8382,INTERREGIONAL MIGRATION IN SPAIN: A SEMIPARAMETRIC ANALYSIS,2004.0,,A. Maza; J. Villaverde,"This paper analyses the determinants of internal migration in Spain between 1995 and 2002. After a brief descriptive study, we present an analytical model of internal migration flows. Subsequently, we estimate this model by applying semiparametric techniques. The general conclusion that we come to is that net migration rates are influenced mainly by income and climatic condition differentials between the regions of origin and destination; in addition, unemployment and housing price differentials seem to have a much weaker effect while variables such as aggregate unemployment, on the one hand, and human capital and population density differentials, on the other, do not affect net migration rates.",21.0,https://rrs.scholasticahq.com/article/8382.pdf 5475c6a1860361ad516bbfeec322ac228c57776a,10.1016/j.jbankfin.2021.106318,Housing networks and driving forces,2022.0,Journal of Banking & Finance,S. Hurn; Shu-Ping Shi; B. Wang,Abstract This paper investigates patterns in housing market networks using Australian and Chinese data and a novel econometric approach based on pairwise time-varying Granger causality tests. The focus is on four fundamental questions. (1) Have housing markets become increasingly connected over time? (2) Does housing market connectivity increase or decrease with house prices? (3) Are socio-economic and geographic proximity important for housing market connections? (4) Do economic fundamentals or sentiment drive connectivity? The results reveal interesting differences in these markets and suggest that one size is not likely to fit all in terms of housing market policy.,21.0,https://www.sciencedirect.com/science/article/pii/S0378426621002685/pdf 57461efe68605e35524b9ded79afad490d79f279,10.1080/09640568.2018.1560930,Public acceptance towards waste-to-energy power plants: a new quantified assessment based on “willingness to pay”,2019.0,Journal of Environmental Planning and Management,Chuan-Wang Sun; Xiaoling Ouyang; Xiaochun Meng,"This article estimates the impact of residential risk perception on the willingness to pay (WTP) to avoid having waste-to-energy (WTE) power plants in the neighborhood. The filed survey shows that 63.4% of the respondents protested for various reasons. It is noteworthy that house owners indicate a greater willingness for resettlement than house renters. However, residents who take WTE incineration facilities into consideration before settlement are more inclined to oppose the payment for facility relocation. Determinants of protest and supportive responses are evaluated by the sample selection method (SSM). Empirical results show that the average WTP for avoiding WTE facilities being sited in the neighborhood is 2,160 CNY, which is reliable because we include protest responses in the estimation process. Housing price, gender, and living area are decisive factors for the WTP value; in particular, the marginal effect of exaggerated risk perception on WTP amounts to 1,074 CNY.",21.0, 2c9b2681340257ea34844a734bdd36c311dbc237,10.1109/iccubea.2018.8697402,Comprehensive Analysis of Housing Price Prediction in Pune Using Multi-Featured Random Forest Approach,2018.0,International Conference on Computing Communication Control and automation,Rushab Sawant; Yashwant Jangid; Tushar Tiwari; Saurabh Jain; Ankita Gupta,"The housing sector in India has been predicted to grow at 30-35% over the next decade. In terms of employment provided, it is second only to the agricultural sector. Housing is one of the major domain of real estate. Pune is emerging as one of the major metropolitan cities of India and has many prestigious Educational institutions and IT parks. This makes it an ideal place to buy homes. Vagueness among the prices of houses makes it challenging for the buyer to select their dream house. The interests of both buyer and seller should be satisfied so that they do not overestimate or underestimate price. This housing price prediction model acts as a helping hand for buyer and seller or a real estate agent to make a better-informed decision. To achieve this, diverse features are selected as input from feature set and various algorithms are applied such as Random Forest and Decision Tree.",21.0, e9c6a16fffab7ab3c2562ec99af0b0539cd3ee8c,10.3368/le.95.3.391,Is Airport Proximity an Amenity or Disamenity? An Empirical Investigation Based on House Prices,2019.0,Land Economics,Ermanno Affuso; S. B. Caudill; F. Mixon; Kevin W. Starnes,"We use a spatial autoregressive model with directional effects to assess the impact of airport noise on housing values in Memphis, Tennessee, home of the busiest cargo airport in the United States. Our model is combined with a spatial dataset that contains information on noise levels, property characteristics, and neighborhood characteristics for 9,606 properties sold between 2011 and 2016. Results of our research suggest that the Memphis International Airport is perceived as a disamenity, with areas of the city affected to different degrees, with a potential average external cost of $4,795 per decibel of noise per household. (JEL C31, D62)",21.0, ab6b63b8285fc53260cff22fba7c40fe39d3b8fd,10.2139/ssrn.1090147,PREDICTING HOUSE PRICES WITH SPATIAL DEPENDENCE: IMPACTS OF ALTERNATIVE SUBMARKET DEFINITIONS,2008.0,,Steven C. Bourassa; E. Cantoni; M. Hoesli,,21.0,https://doi.org/10.2139/ssrn.1090147 994920bae87a9dc1df6f28ae713e52c11e5c1a35,10.1007/978-3-642-32172-6_6,Multivariate Regression Modeling for Home Value Estimates with Evaluation Using Maximum Information Coefficient,2013.0,,Gong-Zhu Hu; Jin-Ping Wang; Wenying Feng,,21.0,http://www.acisinternational.org/springer/samplepaper.pdf 87e99ac95b18d1756a750f6e261c29ecc6a63c82,10.1080/00779959709544273,The determinants of house prices in New Zealand: An aggregate and regional analysis,1997.0,,B. O'Donovan; David Rae,,21.0, 4035ce9ff54bfe95be4d89dd6deb485c74beffe6,10.3389/fpubh.2022.1032668,"Street centrality and vitality of a healthy catering industry: A case study of Jinan, China",2022.0,Frontiers in Public Health,Yan-Bin Chen; G. Yin; Yi-Ming Hou,"In the context of an overall improvement in the national economy, residents' demand for nutrition and health has been increasing. An industry that provides healthy eating plays an increasingly important role in urban development. Few studies, however, have focused on the relationship between the urban road network structure and the vitality of the healthy catering industry (VHCI). Based on social media data and urban traffic network data, in this study, we explored the impact mechanism of street centrality on the VHCI through a case study of Jinan, China, using geographic detectors and multiscale geographically weighted regression (MGWR) methods. The results show the following: First, the vitality of the catering industry in the main urban area of Jinan has multicore spatial distribution characteristics, and the high-value areas of the vitality of the catering industry are highly matched with the main business districts in the city in space. Second, we found clear spatial differences in street centrality between the walking and driving modes. With an increase in the search radius, the trend of high-value areas closely gathering in the urban center became clearer. The distribution o",20.0,https://www.frontiersin.org/articles/10.3389/fpubh.2022.1032668/pdf 404393d7f51437b49c64cc67a68e01d919e52b0f,10.4000/cybergeo.22644,La dynamique spatio-temporelle des prix immobiliers à différentes échelles : le cas des appartements anciens à Paris (1990-2003),2009.0,,M. Guérois; R. Le Goix,"Cet article vise a aborder la complexite des mutations urbaines en cours et propose une experimentation methodologique appliquee aux valeurs immobilieres. Celles-ci traduisent non seulement les caracteristiques intrinseques d’un logement, mais aussi les qualites d’un lieu, evaluees et percues a differents echelons (situation dans l’agglomeration, caracteristiques du quartier et de la rue). L’evolution des prix est de plus un puissant determinant du tri social qui s’opere dans les cœurs urbains. Afin de degager l’interet d’une telle investigation experimentale a fine echelle, nous presentons dans cet article une analyse spatiale des valeurs immobilieres des logements dans Paris intra-muros entre 1990 et 2003. Dans un contexte de baisse puis de hausse soutenue des prix a partir de 1997, il s’agit d’analyser le jeu des reajustements spatiaux des valeurs immobilieres afin d’identifier les dynamiques de depreciation et de valorisation relatives qui sont a l’œuvre. Nous degageons tout d’abord differents profils d’evolution des prix sur cette decennie, en pretant attention a leur inscription spatiale. Nous abordons ensuite la dynamique des discontinuites de cette geographie des prix, en s",20.0,https://doi.org/10.4000/cybergeo.22644 6e4338d9588addf406c787f31f3855d59264d8ef,10.1038/s41366-021-00836-z,Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain,2021.0,International Journal of Obesity,James H. Buszkiewicz; J. Bobb; P. Hurvitz; David E. Arterburn; A. Moudon; A. Cook; S. Mooney; Maricela Cruz; Shilpi Gupta; P. Lozano; D. Rosenberg; M. Theis; Jane Anau; A. Drewnowski,"To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults. Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18–64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values. Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 year",20.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592117 8f8f387da0331232918b7de0ccb67e97a7441d39,10.1371/journal.pntd.0011075,Features of the urban environment associated with Aedes aegypti abundance in high-rise public apartments in Singapore: An environmental case-control study,2023.0,PLoS Neglected Tropical Diseases,S. Fernandez; Haoyang Sun; B. Dickens; L. Ng; A. Cook; J. Lim,"Aedes aegypti abundance in residential estates is hypothesized to contribute to localised outbreaks of dengue in Singapore. Knowing the factors in the urban environment underlying high Ae. aegypti abundance could guide intervention efforts to reduce Ae. aegypti breeding and the incidence of dengue. In this study, objective data on Ae. aegypti abundance in public apartment blocks estimated by Singapore’s nationally representative Gravitrap surveillance system was obtained from the National Environmental Agency. Low and high abundance status public apartment blocks were classified based on the Gravitrap Aegypti Index, corresponding to the lowest and highest quartiles respectively. An environmental case-control study was conducted, wherein a blinded assessment of urban features hypothesised to form breeding habitats was conducted in 50 randomly sampled public apartment blocks with low and high abundance statuses each. Logistic regression was performed to identify features that correlated with abundance status. A multivariable logistic model was created to determine key urban features found in corridors and void decks which were predictive of the Ae. aegypti abundance status of the pub",20.0,https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0011075&type=printable ab04d6af36fb76f4f27c846735e9e37a51f8e7fd,10.1016/j.erss.2020.101505,The wandering energy stars: The challenges of valuing energy efficiency in Australian housing,2020.0,,G. Warren-Myers; C. Kain; K. Davidson,"Abstract In the context of curbing greenhouse gas emissions to meet the ambitious but much-needed targets to limit global warming to reasonable levels, residential housing can play a significant role. By enhancing the energy efficiency of residential housing, emission reductions, reduced costs and better health and wellbeing of occupants may be achieved. Despite gradual improvements in home energy efficiency, homeowner engagement could be greatly enhanced by the demonstration of how energy efficiency and energy ratings can be reflected in residential property values. Ascertaining the value of energy efficiency may enable greater investment and potential for increased availability of finance for added energy efficiency options in new and existing housing. Extant research suggests a significant relationship between house values, rental prices and energy efficiency certifications, particularly where mandatory disclosure operates. However, limited research has examined residential valuers’ (appraisers) perspectives and practice considerations. This paper examines the challenges for valuers in identifying and considering energy efficiency initiatives and ratings on residential property ",20.0,http://hdl.handle.net/11343/251400 e72973af9465c25d12476309f3db57aeacec87ee,10.1155/2020/5375206,The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective,2020.0,,Yan Li; Zhao-Yang Xiang; Tao Xiong,"The first-hand house price in Beijing, the capital of China, has skyrocketed with 43 percent annual growth from 2005 to 2017, exerting tremendous adverse effects on people’s livelihood and the development of real estate. Thus, exploring the behavioral mechanism and accurate forecasts of house prices is a critical element in making decisions under uncertain conditions and is of great practical significance for both participants and policymakers in real estate. According to the complex features of house price, including nonlinear, nonstationary, and multiscale, and considering the remarkable time and frequency discrimination capability of multiscale analysis in dealing with house price problems, we develop an ensemble empirical mode decomposition- (EEMD-) based multiscale analysis paradigm to investigate the behavioral mechanism and then obtain accurate forecasts of house prices. Specifically, the monthly house price in Beijing over the period January 2005 to November 2018 is first decomposed into several different time-scale intrinsic-mode functions (IMFs) and a residual via EEMD, revealing some interesting characteristics in house price volatility. Then, we compose the IMFs and res",20.0,https://downloads.hindawi.com/journals/ddns/2020/5375206.pdf 29b748f41f25e41048b982f1a44dfd6853e72c7e,10.1016/j.amepre.2019.03.020,"Geographic, Temporal, and Sociodemographic Differences in Opioid Poisoning.",2019.0,American Journal of Preventive Medicine,E. Schoenfeld; George S. Leibowitz; Yu Wang; Xin Chen; W. Hou; Sina Rashidian; M. Saltz; J. Saltz; Fu-Sheng Wang,"INTRODUCTION Not enough is known about the epidemiology of opioid poisoning to tailor interventions to help address the growing opioid crisis in the U.S. The objective of this study is to expand the current understanding of opioid poisoning through the use of data analytics to evaluate geographic, temporal, and sociodemographic differences of opioid poisoning- related hospital visits in a region of New York State with high opioid poisoning rates. METHODS This retrospective cohort study utilized patient-level New York State all-payer hospital data (2010-2016) combined with Census data to evaluate geographic, patient, and community factors for 9,714 Long Island residents with an opioid poisoning-related inpatient or outpatient hospital facility discharge. Temporal, 7-year opioid poisoning rates and trends were evaluated, and geographic maps were generated. Overall, significance tests and tests for linear trend were based upon logistic regression. Analyses were completed between 2017 and 2018. RESULTS Since 2010, Long Island and New York State opioid poisoning hospital visit rates have increased 2.5- to 2.7-fold (p<0.001). Opioid poisoning hospital visit rates decreased for men, w",20.0,http://www.ajpmonline.org/article/S0749379719301904/pdf 8373b1bf3d0bd68eb7f5cd5dfa3f927e20a212ee,10.1161/atvbaha.117.310003,Associations Between Residential Proximity to Traffic and Vascular Disease in a Cardiac Catheterization Cohort,2017.0,"Arteriosclerosis, Thrombosis and Vascular Biology",Cavin K. Ward-Caviness; W. Kraus; C. Blach; C. Haynes; Elaine Dowdy; M. Miranda; R. Devlin; D. Díaz-Sánchez; W. Cascio; S. Mukerjee; Casson Stallings; Luther A. Smith; S. Gregory; Svati H. Shah; L. Neas; E. Hauser,"Objective— Exposure to mobile source emissions is nearly ubiquitous in developed nations and is associated with multiple adverse health outcomes. There is an ongoing need to understand the specificity of traffic exposure associations with vascular outcomes, particularly in individuals with cardiovascular disease. Approach and Results— We performed a cross-sectional study using 2124 individuals residing in North Carolina, United States, who received a cardiac catheterization at the Duke University Medical Center. Traffic-related exposure was assessed via 2 metrics: (1) the distance between the primary residence and the nearest major roadway; and (2) location of the primary residence in regions defined based on local traffic patterns. We examined 4 cardiovascular disease outcomes: hypertension, peripheral arterial disease, the number of diseased coronary vessels, and recent myocardial infarction. Statistical models were adjusted for race, sex, smoking, type 2 diabetes mellitus, body mass index, hyperlipidemia, and home value. Results are expressed in terms of the odds ratio (OR). A 23% decrease in residential distance to major roadways was associated with higher prevalence of periphe",20.0,https://www.ncbi.nlm.nih.gov/pmc/articles/5972827 a31e402636848e82c6c17c381e668b331116967d,10.1037/amp0000812,Local housing market dynamics predict rapid shifts in cultural openness: A 9-year study across 199 cities.,2021.0,American Psychologist,F. Götz; T. Ebert; S. Gosling; M. Obschonka; J. Potter; P. Rentfrow,"Accumulating evidence suggests that culture changes in response to shifting socioecological conditions; economic development is a particularly potent driver of such change. Previous research has shown that economic development can induce slow but steady cultural changes within large cultural entities (e.g., countries). Here we propose that economically driven culture change can occur rapidly, particularly in smaller cultural entities (e.g., cites). Drawing on work in cultural dynamics, urban economics, and geographical psychology, we hypothesize that changes in local housing prices-reflecting changing availability of local amenities-can induce rapid shifts in local cultures of Openness. We propose two mechanisms that might underlie such cultural shifts: selective migration (i.e., people selectively moving to cities that offer certain amenities) and social acculturation (i.e., people adapting to changing amenities in their city). Based on trait Openness scores of 1,946,752 U.S. residents, we track annual changes in local Openness across 199 cities for 9 years (2006-2014). We link these data to annual information on local housing markets, an established proxy for local amenities. To ",20.0,https://eprints.qut.edu.au/230125/1/108832439.pdf f9970005577a9a8e46b38055ab849c52ff8ad304,10.3390/rs13122323,"Remote Sensing-Based Analysis of Urban Landscape Change in the City of Bucharest, Romania",2021.0,Remote Sensing,C. Nistor; Marina Vîrghileanu; Irina Cârlan; B. Mihai; Liviu Toma; B. Olariu,"The paper investigates the urban landscape changes for the last 50 years in Bucharest, the capital city of Romania. Bucharest shows a complex structural transformation driven by the socialist urban policy, followed by an intensive real-estate market development. Our analysis is based on a diachronic set of high-resolution satellite imagery: declassified CORONA KH-4B from 1968, SPOT-1 from 1989, and multisensor stacked layers from Sentinel-1 SAR together with Sentinel-2MSI from 2018. Three different datasets of land cover/use are extracted for the reference years. Each dataset reveals its own urban structure pattern. The first one illustrates a radiography of the city in the second part of the 20th century, where rural patterns meet the modern ones, while the second one reveals the frame of a city in a full process of transformation with multiple constructions sites, based on the socialist model. The third one presents an image of a cosmopolitan city during an expansion process, with a high degree of landscape heterogeneity. All the datasets are included in a built-up change analysis in order to map and assess the spatial transformations of the city pattern over 5 decades. In order ",20.0,https://www.mdpi.com/2072-4292/13/12/2323/pdf?version=1623578647 4c50d0ebfcd64d89d724a19f77c057af36cd5e7b,10.1177/0308518x20942874,Marginal and average prices of land lots should not be equal: A critique of Glaeser and Gyourko’s method for identifying residential price effects of town planning regulations,2019.0,Environment and Planning,Cameron K. Murray,"Glaeser and Gyourko’s method of measuring the gap between marginal and average land prices of housing lots has become a popular way of demonstrating the degree to which planning controls, or ‘regulatory taxes’, increase residential land prices. This has led policy-makers across the globe to focus on town planning as a critical determinant of rising home prices. We show, however, that Glaeser and Gyourko’s method shows no such thing. Instead, the price gap is a product of the location premium of land, diminishing returns to buyers of residential land size, and historical city development patterns. Numerous shortcomings are identified in their theoretical model, including that (a) they ignore that development happens over time; (b) their ‘regulatory tax’ is simply the location value of land; (c) they reason inconsistently about the source of land prices, arguing that land at the margins is scarce but locations for whole housing lots are not; and (d) there are no optimal lot sizes nor subdivision incentives in their model. Standard price-taking models of residential land markets that recognize that locations are scarce contain none of these limitations and provide a better interpretat",20.0,https://doi.org/10.2139/ssrn.3328308 d1aecc134aa564d428da0f76c615e1a88f264c17,10.1109/access.2021.3077860,Can Web Search Queries Predict Prices Change on the Real Estate Market?,2021.0,IEEE Access,Nina Rizun; Anna Baj-Rogowska,"This study aims to explore whether the intensity of internet searches, according to the Google Trends search volume index (SVI), is a predictor of changes in real estate prices. The motivation of this study is the possibility to extend the understanding of the extra predictive power of Google search engine query volume of future housing price change (shift direction) by (i) the introduction of a research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying the multi-class HPI values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the SVI that relates to interests in both ‘real estate’ and ‘credit to buy real estate’; (iv) evaluation of the introduced approach in the context of the Polish real estate market. The main theoretical contribution of our work is a confirmation that the freely available information regarding Google user searches can provide an in-depth insight into enriching the generally accepted statistics on supply and demand in the real estate market. From the practical perspective, this research confirms ",20.0,https://doi.org/10.1109/access.2021.3077860 4b5caf1e97d7647eceb2c88cefd554746d9ac62d,10.3390/ijerph16214072,Effects of Freeway Rerouting and Boulevard Replacement on Air Pollution Exposure and Neighborhood Attributes,2019.0,International Journal of Environmental Research and Public Health,Regan F. Patterson; R. Harley,"Freeway rerouting and replacement with a street-level boulevard are urban transportation policies, that may help redress disproportionate air pollution burdens resulting from freeway construction that took place during the mid-20th century. However, environmental justice activism for freeway rerouting and urban green space creation may have the unintended consequence of environmental gentrification. In this paper, we investigate the effects of freeway routing decisions on exposure to traffic-related air pollution and neighborhood socioeconomic and demographic change. We focus on the effects of rerouting the Cypress Freeway in West Oakland, along with the construction of a street-level boulevard (Mandela Parkway), on the original freeway alignment. The impacts of two rebuild scenarios, freeway rebuild-in-place and reroute, on near-roadway NOx and BC concentrations are compared. We also assess changes in demographics and land use in West Oakland, between the time when the Cypress Freeway was damaged by a major earthquake and after completion of Mandela Parkway. Our research indicates that freeway rerouting reduced annual average concentrations of both NOx (−38% ± 4%) and BC (−25% ± 2",20.0,https://www.mdpi.com/1660-4601/16/21/4072/pdf?version=1571832470 c857bda36784018ccc5edfd8b5c4a3178bd8bc70,10.3390/su12030801,The Influence of Traffic Noise on Apartment Prices on the Example of a European Urban Agglomeration,2020.0,,A. Szczepańska; A. Senetra; Monika Wasilewicz-Pszczółkowska,"Traffic noise is one of the key determinants of apartment prices. The real estate market is highly sensitive to adverse environmental factors, which explains significant variations in apartment prices. The aim of this study was to identify the correlations between traffic noise levels and the prices of apartments traded on a local real estate market. The study analyzed three residential districts in the city of Olsztyn in north-eastern Poland. The database covered 5259 transactions concluded in 2005–2018. The collected data were standardized in terms of technical and functional parameters, and noise was the key factor differentiating the evaluated property. The relationships between the explained variable (apartment price) and the explanatory variable (traffic noise) were determined by linear correlation analysis. A negative correlation was noted, which confirms that a building’s location relative to the road network and, consequently, the level of traffic noise, is one of the factors that play a role in the potential buyers’ choice of property. The results of the study also indicate that the impact of traffic noise on the decisions made by real estate buyers continued to decrease ",20.0,https://www.mdpi.com/2071-1050/12/3/801/pdf?version=1579874064 00d11312871963fd0180da4f696b28081ecfa66d,10.1038/s41598-022-12941-8,Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore,2022.0,Scientific Reports,Murali Krishna Gurram; Min-Xiang Wang; Yi-Chen Wang; Jun-Xiong Pang,"Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5–1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) w",20.0,https://www.nature.com/articles/s41598-022-12941-8.pdf 776391cd14c2fb615c3825167dce5c61172f0b06,10.3390/healthcare11222979,The Impact of Non-Adherence to Antihypertensive Drug Therapy,2023.0,Healthcare,Syed Karam Gardezi; W. Aitken; M. Jilani,"Medication non-adherence is a major healthcare barrier, especially among diseases that are largely asymptomatic, such as hypertension. The impact of poor medication adherence ranges from patient-specific adverse health outcomes to broader strains on health care system resources. The Centers for Disease Control (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) database was used to retrieve Centers for Medicare and Medicaid Services’ data pertaining to blood pressure (BP) medication adherence, socio-economic variables, and cardiovascular (CV) outcomes across the United States. Multivariable linear regression models were used to estimate the change in total CV deaths as a function of non-adherence to BP medications. For every percent increase in the non-adherence rate, the total number of CV deaths increased by 7.13 deaths per 100,000 adults (95% CI: 6.34–7.92), even after controlling for the percentage of residents with access to insurance, the percentage of residents who were eligible for Medicaid, the percentage of residents without a college education, median home value, income inequality, and the poverty rate (p < 0.001). There is a significant association betwee",20.0,https://www.mdpi.com/2227-9032/11/22/2979/pdf?version=1700274984 3022f7f6a987e652d0e41f402d5726596e1e3abc,10.3390/su132112252,The Spatial Distribution and Influencing Factors of Urban Cultural and Entertainment Facilities in Beijing,2021.0,Sustainability,Dan He; Zi-Xuan Chen; Shao Ai; Jing Zhou; Lin-Lin Lu; Tingru Yang,"Cultural and entertainment facilities are an important mainstay for urban development and the well-being of urban residents. Studying their spatial distribution is thus of great significance for improving urban functions and shaping urban characteristics. This paper uses the Simpson index, grid method, kernel density, nearest neighbor analysis and hierarchical clustering analysis to present in detail the spatial pattern, hotspot distribution and clustering characteristics of urban cultural and entertainment facilities in Beijing. With the help of the spatial lag model, the main factors affecting the spatial distribution of the facilities are explored. The results are as follows: Different types of cultural and entertainment facilities have different spatial agglomeration effects, which are closely related to the historical background of Beijing, industrial distribution, and the living needs of residents; the facilities generally present a spatial distribution with prominent centrality, strong clustering and significant heterogeneity; and financial insurance institution density, building density, securities company density, housing rent and distance to nearest scenic spot are the ma",20.0,https://www.mdpi.com/2071-1050/13/21/12252/pdf?version=1638945147 e219a3354ee0c17bda293701d626c6deed9ec0c3,10.3390/ijgi11020129,"Simulating the Spatial Heterogeneity of Housing Prices in Wuhan, China, by Regionally Geographically Weighted Regression",2022.0,ISPRS Int. J. Geo Inf.,Zengzheng Wang; Yangyang Zhao; Fuhao Zhang,"Geographically weighted regression (GWR) is an effective method for detecting spatial non-stationary features based on the hypothesis of proximity correlation. In reality, especially in the social and economic fields, research objects not only have spatial non-stationary characteristics, but also spatial discrete heterogeneity characteristics. Therefore, how to improve the accuracy of GWR estimation in this case is worth studying. In this paper, a regionally geographically weighted regression (RGWR) is proposed. Using incoming dummy variables, the zoning discrimination is added to the spatial kernel function of GWR, the spatial kernel function is modified, the spatial weight is optimized, and the influence of “near heterogeneous” observation points is reduced. In this paper, the residential sale price in Wuhan City is taken as an example in the analysis of three aspects: model performance, fitting effect and influencing factors. The results show that the introduction of a zoning dummy variables can significantly improve the model accuracy of a fixed bandwidth and adaptive bandwidth. Under a fixed bandwidth, compared with the GWR model, RGWR increases R2 and R2adj from 0.6776 and 0.",20.0,https://www.mdpi.com/2220-9964/11/2/129/pdf?version=1645067816 ba09e75417d71ebe528d6f28627c5eaab90a8910,10.3390/su11247165,The Role of Planning Policies in Promoting Urban Sprawl in Intermediate Cities: Evidence from Chile,2019.0,,J. Barton; M. I. Ramírez,"Urban sprawl has been studied principally as a phenomenon produced by a lack of or weakness in urban planning, as a consequence of real estate liberalization. This article examines the Chilean case, and proposes that the state has been the engine of this phenomenon through spatial planning instruments that have both neoliberal and neostructural features, and that are best defined by the concept, new public management. The analysis tracks urban sprawl in four intermediate cities, which have experienced high rates of growth since 2000, using photointerpretation of satellite images between 2003 and 2011, and the creation of a typology to define land uses and housing types. The results show that intermediate cities follow similar trends to the capital city, Santiago, and face similar problems, in particular the concentration of services in the urban core. These similarities are produced by the application of general planning instruments: Article 55 and Decree Law 3516. While most research on urban sprawl focuses on private agency, this article highlights the role of the state in its production. It is therefore relevant to explore the nature of public agency in urban sprawl processes in",20.0,https://www.mdpi.com/2071-1050/11/24/7165/pdf?version=1576493499 5fecfd757bb0b489146485bcfd550f94989d2df3,10.1371/journal.pone.0278047,"Money attitudes, financial capabilities, and impulsiveness as predictors of wealth accumulation",2022.0,PLoS ONE,M. Fenton-O'Creevy; A. Furnham,"In this study we examined three correlates of personal wealth–financial capability, buying impulsiveness, and attitudes to money in a large UK adult sample (N = 90,184). We were interested in how these psychological variables related to personal wealth controlling for well-established demographic correlates: age, education, gender, and household income. We drew on three personal wealth variables based on savings and investments, property wealth and personal items. Using correlational and regression analysis we tested three specific hypotheses which each received support. Our variables accounted for around half the variance with respect to property value, and two thirds with respect to investments. The hierarchical regression onto the savings and investment factor showed two thirds of the variance was accounted for: the demographic variables accounted for 27% of the variance, money attitudes an additional 14%; financial capability an additional 24% and buying impulsivity no additional variance. Age, income, and planning ahead were the most powerful and consistent predictors of wealth variables, with associating money with security as an important predictor for savings and investment",20.0,https://doi.org/10.1371/journal.pone.0278047 2544f65f87a1ceade603c71a2c3a9eca4817d6bf,10.14311/cej.2017.04.0044,"HOW MUCH DOES A MINUTE OF COMMUTING TIME COST? AN EXAMINATION OF PROPERTY PRICES IN RELATION TO DISTANCE TO THE CITY CENTER IN PRAGUE, CZECH REPUBLIC",2017.0,,Martin Lukavec; B. Kadeřábková,"This paper sets out to explore the strength of the relationship between the proximity of a property to the city center and its price. Buyers are willing to pay extra for apartments or houses closer to the city center, but the extent of this willingness remains largely unexplored. Our research question is: How much does a minute of commuting time influence the price of an apartment in Prague? In other words, with every minute of commuting time, how much more is paid for a house or an apartment closer to the central business district (CBD)? Our analysis has found that on average, every minute of commuting time closer to the city center corresponds to an additional cost of CZK 43,390.45 for an average sized apartment in Prague. A regression analysis is graphically plotted in the Chart 1. We have also found that this relationship changes according to distance from the city center. For a commuting time of 1-20 minutes to the city center, the price increase is the highest: CZK 259,466.18 per minute. However, this figure is only CZK 55,809.01 for the interval of 21-40 minutes, and CZK 33,924.29 per minute for the interval of 41-55 minutes.",20.0,https://doi.org/10.14311/cej.2017.04.0044 a789e273701524ab53250f325f8828bb0025fdcd,10.1016/j.jhe.2020.101692,Is Housing the Business Cycle? A Multiresolution Analysis for OECD Countries,2020.0,,Yuting Huang; Qiang Li; K. Liow; Xiao-Xiao Zhou,"Abstract Fluctuations in housing markets 1 have long been recognized as leading indicators of an economy. Leamer (2007, 2015) claimed that housing activities predict and cause national business cycles in the United States. We investigate his claims for a larger set of countries. We also examine this predictive relationship in multiple resolutions or timescales, namely, short-run and long-run variations of business cycles. Structural vector auto-regression (SVAR) models are estimated to test his more contentious causal claim. Our results show that housing indicators lead business cycles in most countries and that leading relationship is more prominent in the long run. In addition, our SVAR results for the United States indicate that housing factors are likely independent drivers of business cycles. Housing supply indicators predict short-run variations in business cycles, and housing price predicts long-run variations better. The cross-country evidence is less certain. Generally, our findings on timescale-based relationships between housing and the macroeconomy restrict future models of business cycles.",20.0,https://figshare.com/articles/journal_contribution/Is_housing_the_business_cycle_A_multiresolution_analysis_for_OECD_countries/20698621 ed82ae8547cc5c5974ab6a335ec4b2543486d2c9,10.7172/2449-6634.jmcbem.2017.1.1,"The Use of Artificial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey",2017.0,,O. Ki̇tapci; Ömür Tosun; M. Tuna; T. Türk,"The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artifi cial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer – one hidden layer – output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artifi cial neural networks (ANN) proposed here will serve as a reference for countries that develop artifi cial neural networks (ANN) method-based housing price determination in future. Applying the artifi cial neural networks (ANN) approach for estimation of housing prices is relatively new in the fi eld of housing economics. Moreover, this is the fi rst study that uses the artifi cial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey.",20.0,https://doi.org/10.7172/2449-6634.jmcbem.2017.1.1 ce782b6be542ab1929cbf2850761d10f604cdfbe,10.1016/j.cbrev.2019.05.001,The impact of income distribution on house prices,2019.0,Central Bank Review,M. U. Özmen; M. Koray Kalafatcılar; Erdal Yılmaz,"Abstract Housing market developments have been attracting a great deal of attention in Turkey. Concerns related to supply, particularly at the higher segment of the market, lead these discussions. In this respect, basic regression estimations indicate that over the post-2010 period, income elasticity of house price changes is negative, despite housing being a normal good. In order to reveal the underlying reason, we discuss the role of income distribution. Our empirical analyses suggest that the share of bottom (the top) income quintiles are positively (is negatively) correlated with house price changes. Given the current ample stock of houses, at the high-end of the market, a demand surge led by an increase in the income share of the top income quintile may not put pressure on house prices. In addition, the declining income share of bottom three income quintiles may lead to a reduction in their housing demand. Thus, policies directed to improving income equality might help mitigate the imbalances in the housing market.",20.0,https://www.sciencedirect.com/science/article/pii/S1303070119300538/pdf d137401873bf1541045d38c808ce74579ad4b084,10.3390/su10020343,"Immigration and the Housing Market: The Case of Castel Volturno, in Campania Region, Italy",2018.0,,F. Forte; V. Antoniucci; P. De Paola,"According to Eurostat, Italy is the fifth country of the European Union per immigrant population. The complexity of the phenomenon, as it has evolved in recent years, leads to analyzing it from a specific point of view, that of the real estate market. The article represents the early stage of research on the housing condition of the immigrant population in the Southern Italy and its effect on the housing market. First, we describe the spatial segregation phenomenon affecting the immigrant population in Campania Region; then we analyze data of the municipality of Castel Volturno, which has one of the greater migratory pressure throughout the whole region. We provide statistical regressions correlating housing prices and socio-economic features from 2006 to 2016. The results confirm the findings of the current literature on the subject: there is a specific phenomenon associated with the presence of an immigrant population residing in conjunction with a reduction of housing prices.",20.0,https://www.mdpi.com/2071-1050/10/2/343/pdf?version=1517224948 6e4be5daf59355440452370cc31893fbd8320793,10.1111/risa.13136,Assessing the Economic Benefits of Flood Defenses: A Repeat‐Sales Approach,2018.0,Risk Analysis,Allan Beltrán; D. Maddison; R. Elliott,"In this article we identify the impact of the construction of flood defenses on property prices using a difference‐in‐differences repeat‐sales methodology. Our data set contains information on over 12 million individual property transactions, which is merged with GIS data identifying the spatial location and main characteristics of 1,666 flood defenses built in England between 1995 and 2014. Results suggest that at the finer 6‐digit postcode level the construction of flood defenses raises urban house prices by 12.6% to 16.7%. However, for rural properties at the slightly coarser 5‐digit postcode level the construction of defenses reduces house prices by 0.8% to 5.0%. This suggests that in certain locations the disamenity impact of flood defenses and the perceived threat of redirected flooding outweigh the benefits of reduced flood risk.",20.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/risa.13136 7cc080e6035f5ca684924692878aa4dbf9699930,10.1108/ijhma-09-2023-0120,Comparative analysis of machine learning models in predicting housing prices: a case study of Prishtina's real estate market,2024.0,International Journal of Housing Markets and Analysis,Visar Hoxha," Purpose The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina. Design/methodology/approach Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test. Findings Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables. Originality/value To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in ",20.0, f9deaf7e94d67974d4e6632f2aba48e74e2811dc,10.18488/journal.8/2016.4.2/8.2.104.111,An Empirical Analysis of Factors Affecting the Housing Price in Shanghai,2016.0,,Yufang Wang; Yan-Qing Jiang,"Being one of the most booming and prosperous cities of China, Shanghai is attracting more and more talents from all over the world. Although the development of real estate market contributes a lot to the economic take-off, the excessively high the housing price may cause big troubles. In order to find the main factors affecting the housing price in Shanghai, an econometric model is established and seven explanatory variables, per capita disposable income in Shanghai, land transaction price index, construction cost, urbanization rate, interest rate, CPI of residence and investment in real estate in Shanghai are taken into consideration based on the supply and demand theory. With the help of regression analysis, only interest rate is kicked out and the other six can all be a good explanatory variable for the housing price.",20.0,https://doi.org/10.18488/journal.8/2016.4.2/8.2.104.111 3af53533fdb415e445e82c9304d229a03b88cd17,10.3846/1648715x.2015.1124148,Ripple effect and regional house prices dynamics in China,2016.0,,Chi-Chuan Lee; Chien-Chiang Lee; Shu-hen Chiang,"This paper examines the stationarity properties, the long-run equilibrium and the leadlag relationship among the regional house prices in China from December 2000 to July 2013. Unlike traditional unit-root tests, the panel seemingly unrelated regressions augmented Dickey-Fuller (SURADF) unit-root test reveals that the regional house prices in China are a mixture of I(0) and I(1) processes. There is concrete evidence in favor of the hypothesis of a long-run equilibrium relationship among all regions, except for Shanghai region, and supporting the price diffusion or ripple effect among these Chinese cities. Finally, we determine that these regional house prices exhibit uni-directional causalities running from Beijing, Chongqing, and Shenzhen to Guangzhou and Tianjin, respectively.",20.0,https://journals.vgtu.lt/index.php/IJSPM/article/download/2099/1694 99994982158adc5d4e3279cdcde54bc9144a5d79,10.1111/tgis.12536,A deep learning approach for rooftop geocoding,2019.0,Trans. GIS,Zhengcong Yin; An-Dong Ma; Daniel W. Goldberg,"Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different ",20.0, f930bafd14e8fb7eeaa19f13d4ed1737e10157f8,10.1146/annurev-economics-020723-041113,"Understanding Spatial Agglomeration: Increasing Returns, Land, and Transportation Costs",2024.0,Annual Review of Economics,H. R. Koster; Jacques-François Thisse,"Economic activities are concentrated on a small share of inhabitable land. In our view, this agglomeration is the outcome of a trade-off between increasing returns and transportation costs, which capitalizes into land rents. Our second baseline idea is that Tiebout-like sorting provides a general framework to handle a large array of problems in spatial economics. Cities have high housing prices because they are productive and offer high levels of amenities and public goods. Both production and amenity effects capitalize in the land rent at a particular location. Through the process of bidding for land, spatial sorting is the involuntary consequence of a myriad of individual decisions made by agents who pursue their own interests.",20.0,https://doi.org/10.1146/annurev-economics-020723-041113 0d6bc73ac5588c938604152251b9d2b09cf958d6,10.1109/taai51410.2020.00047,A Gradient Boosting Method for Effective Prediction of Housing Prices in Complex Real Estate Systems,2020.0,International Conference on Technologies and Applications of Artificial Intelligence,B. Almaslukh,"Analyzing real estate market changes by different parties and agencies that have a significant effect on real estate health and trends. In complex real estate systems, the prediction of housing prices plays an important role in mitigating the impacts of property valuation and economic growth. Several works have proposed the use of various machine learning models for predicting housing prices of real estate markets. However, developing an effective machine learning models to predict the housing prices is still a challenge and needs to be investigated. Therefore, this paper proposes an optimized model based on the gradient boosting (GB) method for improving the prediction of housing prices in complex real estate systems. To evaluate the proposed method, a set of experiments is conducted on a public real estate dataset. The experimental results show that the optimized GB (OGB) method can be used effectively for housing price prediction of real estate and achieves 0.01167 of the root mean square error; the lowest result compared to the other baseline machine learning models.",20.0, b1c555ad302c0dee8f9d8492304d62ac79efc2a3,10.1016/j.jclepro.2019.03.145,Residential landscapes in suburban China from the perspective of growth coalitions: Evidence from Beijing,2019.0,Journal of Cleaner Production,Jia-Yu Wu; Yu-Xiang Wu; Wei Yu; Jian Lin; Qingsong He,"Abstract Residential landscapes in China manifest characteristics different from those seen in Western countries: high density, facilities improvement, and good quality. This study investigates residential landscape by developing a novel framework to capture a growth coalition that is composed of local government and real estate enterprises. Stemming from institutional insights, this study assumes that the growth coalition in China attempts to acquire residential land far from built-up areas to reduce costs, increase the floor area ratio and the green rate, and promote accessibility and walkability to improve housing prices. Taking Beijing as a case, we first identify the residential expansion type (renewal, infill, edge, outlying). Then, two models are created: the land acquisition cost model and the housing price model. The results provide a good argument in support of our hypothesis. This study contributes institutional knowledge to support an understanding of residential landscapes and urban sustainability in China.",20.0, e42270488a4c7818d168f0d4579296d882867fc0,10.1016/j.econlet.2021.109749,The role of housing market in the effectiveness of monetary policy over the Covid-19 era,2021.0,Economics Letters,N. Apergis,"The efficiency of monetary policy substantially depends on the phase of the housing cycle since house prices are important determinants of banks’ willingness to lend. This paper presents evidence on 31 countries which shows that over the pandemic Covid-19 period, in a regime of a strong housing market, the effects of a monetary expansion are smaller than in a regime of low house prices. The findings are important for central banks which have implemented easing monetary policies responding to the Covid-19 pandemic.",20.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754661 3b32d080088cf933b6252aced526927d4ea36162,10.1353/jda.2017.0080,Determinants of house prices and new construction activity: An empirical investigation of the Namibian housing market,2017.0,,Tafirenyika Sunde; Paul-Francois Muzindutsi,,20.0,https://mpra.ub.uni-muenchen.de/86582/1/MPRA_paper_86582.pdf 2f2c4c9104ab5f35fc4278e11f3a1d6ccb5a75ca,10.22300/1949-8276.11.1.84,The Impact of Water front Location on Residential Home Values Considering Flood Risks,2019.0,The Journal of Sustainable Real Estate,N. Miller; Jeremy Gabe; Michael A. Sklarz,"We confirm existing findings of significant premiums for waterfront proximity, more so for oceans, bays, and large lakes than rivers. We then expand research on housing price trends immediately bef...",20.0, d08df985d975e985376df89c2c16beca6f9387b8,10.1007/s10018-015-0136-z,Disamenity impact of Nala Lai (open sewer) on house rent in Rawalpindi city,2017.0,,I. Muhammad,,20.0, f96ae327517a8457e3c1f01c66ddc882f4fe9efd,10.1007/s10901-020-09742-8,"A geographically weighted regression approach to investigating local built-environment effects on home prices in the housing downturn, recovery, and subsequent increases",2020.0,Journal of Housing and the Built Environment,Chih-Hao Wang; Na Chen,,20.0, 57363f365f7588a484192c72c748fb9d109d2b16,10.3390/ijgi10010016,Simulation of the Urban Jobs-Housing Location Selection and Spatial Relationship Using a Multi-Agent Approach,2021.0,ISPRS Int. J. Geo Inf.,Huihui Wang; Weihua Zeng; R. Cao,"The jobs–housing balance concerns the spatial relationship between the number of jobs and housing units within a given geographical area. Due to the separation of jobs and housing, spatial dislocations have occurred in large cities, which have resulted in a significant increase in commuting distance and time. These changes have ultimately led to an increase in pressure on urban traffic, and the formation of tidal traffic. In this study we introduce a multi-agent approach to examine the jobs–housing relationship under the maximum location utility of agents. The jobs/housing ratio measures the balance of the of jobs–housing relationship, as well as comparing and analyzing jobs–housing separation in Beijing by district, county, and street scales. An agent-based model was proposed to simulate spatial location selection behavior of agents by considering environmental and economical influences on residential decisions of individuals. Results show that the jobs–housing relationship imbalance in Beijing has been mainly aggravated due to rapid population growth in the 6th Ring Road. An imbalance in the jobs–housing relationship has arisen due to a mismatch with the number of households avai",19.0,https://www.mdpi.com/2220-9964/10/1/16/pdf?version=1610533663 a8f35aaffce7a19db65aeb4b5f670e30504b4e2f,10.3389/fpubh.2022.994835,Intergenerational differences in the urban vibrancy of TOD: Impacts of the built environment on the activities of different age groups,2022.0,Frontiers in Public Health,Bingjie Yu; Xu Cui; Run-Ze Liu; Pin-Yang Luo; Fangzhou Tian; Tian Yang,"Transit-oriented development (TOD) has been regarded as an effective way to improve urban vibrancy and facilitate affordable, equitable, and livable communities in metro station areas (MSAs). Previous studies placed great attention on the interplay between the MSA-level built environment and overall human activities while neglecting the heterogeneity among different age groups. To address this gap, we leverage the mobile phone signaling data to quantify the spatio-temporal distribution of the MSA-level human activities among different age groups as measured by the vibrancy index (VI). Furthermore, we investigate the impact of the MSA-level built environment on the VI and its intergenerational differences by employing multiple linear regressions based on multi-sourced data. To this end, Chengdu—a TOD-thriving megacity in China—is chosen as a case study. The results indicate that: (1) Residential and bus stop density are positively associated with the VI. And the magnitudes of the correlation coefficients are similar among different age groups. (2) Distance to CBD is negatively associated with the VI of teenagers (12–18 years), middle-aged adults (40–59 years), and older adults (abov",19.0,https://www.frontiersin.org/articles/10.3389/fpubh.2022.994835/pdf fd9ab9831a73c7828954e01e097ddcb6fc47dc3e,10.1108/jpif-11-2021-0095,Why do they go to the peripheries? Studying the relations between the real estate market and coworking spaces in the peripheral areas of Germany,2022.0,Journal of Property Investment & Finance,Thomas M. Vogl; Grzegorz Micek,"PurposeThe study was designed to investigate the bidirectional causation between the real estate market characteristics (residential property prices/rents (including PTR), office rents) and the rise of coworking spaces (CSs) in the peripheral areas of Germany.Design/methodology/approachBased on the desk research, the authors constructed their own database of 1,201 CSs. The authors gathered data on the residential and office prices and rents on a district level. To identify real market differences between districts with and without CSs, the authors applied the t-test for independent samples.FindingsThe second-highest number of CSs were found to operate in the office market peripheries. This phenomenon should be explained by a search for lower office rents, which CSs seek. Most CSs in the peripheral areas of Germany were only recently established in tourist-oriented regions in the south and north of Germany. In this paper, the authors confirmed that the strength of peripheral CSs lies in the hybridity of their operations: for the majority of CSs, running a CS is a non-core business. The authors argue that the role of CSs is rather limited in attracting real estate investors and boost",19.0,https://ruj.uj.edu.pl/xmlui/handle/item/304047 5ab734264a727ff777d521ba6a331d95895867bb,10.1177/2399808318819847,Coupling data science with community crowdsourcing for urban renewal policy analysis: An evaluation of Atlanta’s Anti-Displacement Tax Fund,2018.0,Environment and Planning B Urban Analytics and City Science,Jeremy Auerbach; Christopher Blackburn; Hayley Barton; Amanda Meng; E. Zegura,"We estimate the cost and impact of a proposed anti-displacement program in the Westside of Atlanta (GA) with data science and machine learning techniques. This program intends to fully subsidize property tax increases for eligible residents of neighborhoods where there are two major urban renewal projects underway, a stadium and a multi-use trail. We first estimate household-level income eligibility for the program with data science and machine learning approaches applied to publicly available household-level data. We then forecast future property appreciation due to urban renewal projects using random forests with historic tax assessment data. Combining these projections with household-level eligibility, we estimate the costs of the program for different eligibility scenarios. We find that our household-level data and machine learning techniques result in fewer eligible homeowners but significantly larger program costs, due to higher property appreciation rates than the original analysis, which was based on census and city-level data. Our methods have limitations, namely incomplete data sets, the accuracy of representative income samples, the availability of characteristic trainin",19.0,https://journals.sagepub.com/doi/pdf/10.1177/2399808318819847 25aebab245fe00aace2a149a2161924ed1553888,10.1016/j.compenvurbsys.2018.03.009,The role of subjective risk perceptions in shaping coastal development dynamics,2018.0,"Computers, Environment and Urban Systems",N. Magliocca; M. Walls,"Abstract Social and economic costs in coastal zones resulting from natural hazard events, such as hurricanes, are increasing. Household residential location decisions and adaptive behaviors (i.e., purchasing insurance) are influenced by perceived risk of storms events, and can have long-term consequences for development patterns and regional resilience. Perceived risks may be capitalized into housing prices in hazardous areas, but the attraction of coastal amenities may dampen market responses to risk information. Empirical studies provide contradictory conclusions about the effect of storm events on housing market dynamics, and thus the decision-making processes leading to post-storm residential location and insurance purchase choices remain unclear. Here, an economic agent-based model (ABM) of coupled housing and land markets (CHALMS), adapted to a coastal setting, or C-CHALMS, is used to investigate alternative decision-making models and associated behavioral mechanisms driving post-storm responses. A pattern-oriented modeling (POM) and abductive reasoning approach is used to compare the ability of alternative decision-making models to explain empirical patterns of housing and i",19.0,https://www.sciencedirect.com/science/article/pii/S0198971517305148/pdf 9c2d8f55974ce92eb190cb77150f00fb5bfa6dd6,10.3390/ijgi11060335,"Exploring Spatial Nonstationarity in Determinants of Intercity Commuting Flows: A Case Study of Suzhou-Shanghai, China",2022.0,ISPRS Int. J. Geo Inf.,Zhipeng Li; Xin-Yi Niu,"The increasing popularity of intercity commuting is affecting regional development and people’s lifestyles. A key approach to addressing the challenges brought about by intercity commuting is analyzing its determinants. Although spatial nonstationarity seems inevitable, or at least worth examining in spatial analysis and modeling, the global perspective was commonly employed to explore the determinants of intercity commuting flows in previous studies, which might result in inaccurate estimation. This paper aims to interpret intercity commuting flows from Suzhou to Shanghai in the Yangtze River Delta region. For this purpose, mobile signaling data was used to capture human movement trajectories, and multi-source big data was used to evaluate social-economic determinants. Negative binomial (NB) regression and spatially weighted interaction models (SWIM) were applied to select significant determinants and identify their spatial nonstationarity. The results show that the following determinants are significant: (1) commuting time, (2) scale of producer services in workplace, (3) scale of non-producer services in residence, (4) housing supply in residence, (5) year of construction in res",19.0,https://www.mdpi.com/2220-9964/11/6/335/pdf?version=1654498841 355d7243ad31495bb8cc1e6cf8dc01279163b3ec,10.3390/ijerph17207443,Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities,2020.0,International Journal of Environmental Research and Public Health,Biao Sun; Shan Yang,"Fine particulate matter(PM2.5) pollution will affect people’s well-being and cause economic losses. It is of great value to study the impact of PM2.5 on the real estate market. While previous studies have examined the effects of PM2.5 pollution on urban housing prices, there has been little in-depth research on these effects, which are spatially heterogeneous at different conditional quantiles. To address this issue, this study employs quantile regression (QR) and geographically weighted quantile regression (GWQR) models to obtain a full account of asymmetric and spatial non-stationary effects of PM2.5 pollution on urban housing prices through 286 Chinese prefecture-level cities for 2005–2013. Considerable differences in the data distributions and spatial characteristics of PM2.5 pollution and urban housing prices are found, indicating the presence of asymmetric and spatial non-stationary effects. The quantile regression results show that the negative influences of PM2.5 pollution on urban housing prices are stronger at higher quantiles and become more pronounced with time. Furthermore, the spatial relationship between PM2.5 pollution and urban housing prices is spatial non-station",19.0,https://www.mdpi.com/1660-4601/17/20/7443/pdf?version=1602584325 7f4b8bd90a8dcc52bae3112f45a8654da639a49c,10.3390/su13052963,Reflecting the Sustainability Dimensions on the Residential Real Estate Prices,2021.0,Sustainability,Marilena Mironiuc; E. Ionașcu; M. Huian; Alina Țaran,"The paper analyzes the reaction of residential property prices to sustainability attributes and the extent to which they capitalize the effects of sustainability on real estate markets in EU-28 countries in the period 2000–2018. Given that the sustainable real estate market is mainly driven by demand, the sustainability attributes included in the study reflect both buyers’ expectations and their investment potential in sustainable residential properties, and developers’ efforts to become more “sustainable” through responsible property investment. In order to correspond to the current meaning of sustainable development, the variables capture the four dimensions that give content to the concept of the quadruple bottom line: economic, social, environmental and institutional. Using panel data and the two-stage least squares (2SLS) method, the research reveals a pronounced sensitivity of residential property prices to all sustainability dimensions in countries considered leaders in implementing the Sustainable Development Goals (SDGs), characterized by a strong institutional environment, and efficient and transparent real estate markets. In countries less committed to SGD implementation",19.0,https://www.mdpi.com/2071-1050/13/5/2963/pdf?version=1615361431 933204aac69df35d44733b61b90a1210852e7b68,10.24294/jipd.v1i1.22,A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line,2017.0,,M. Diao; Yi Fan; Tien Foo Sing,"This study uses the opening of a new Mass Rapid Transit (MRT) line, known as the Circle Line (CL), between 2010 and 2012 in Singapore as the exogenous event to empirically test the impact of the new CL on housing wealth of households. Using a differences-in-differences (DID) approach, and with the non-landed private housing transaction data covering the period from 2009 to 2013, we find that the average housing prices in our sample increase by 1.6% in the post-opening of the CL. We also find significant capitalization of the new CL into housing prices, especially households living within a 400 meter radius (the treatment zone) from the closest MRT stations on the CL. The treatment effects that are measured by the marginal willingness to pay for houses located within the treatment zone is 13.2% relative to houses located outside the treatment zone. The new CL opening creates an estimated of S$1.23 billion housing effects for households living in the close proximity to the CL MRT stations. However, we do not find significant “anticipative” effects on house prices in the 6-month window prior to the opening of CL. The strongest treatment effect is found after the opening of the phase 1",19.0,https://systems.enpress-publisher.com/index.php/jipd/article/download/22/27 9a37a75e686f359c4523f5ca7443d46a9877164d,10.3390/su12125073,"The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China",2020.0,Sustainability,Jin Hu; Xu Xiong; Yuanyuan Cai; Feng-Lin Yuan,"The ripple effect of housing price movements between cities has been extensively investigated, but there are relatively few studies on this topic within a metropolitan context, especially at the submarket level. This paper describes the use of ripple effect theory to examine the diffusion process and convergence of intra-urban housing prices at the submarket level in Shanghai, an emerging global city in China. The analysis is based on directed acyclic graphs, local indicators of spatial association time-paths, and a recently developed convergence test. The empirical results of grouping analysis identify 25 submarkets in Shanghai, and the diffusion of housing prices between these submarkets is found to be caused by both geographical and economic proximities. There is also a complex recursive process of price spillovers from high- to low-priced submarkets, and vice versa, which contributes to the spiraling local housing prices. Housing prices diverge across all submarkets, and the whole market can be divided into three convergence clubs. Finally, these convergence clubs have a circular structure with a degree of continuity. This study broadens our knowledge of the price interrelation",19.0,https://www.mdpi.com/2071-1050/12/12/5073/pdf?version=1592837794 57be8411aad588fe679210a8eaa1e5bd5c85aa47,10.1371/journal.pone.0205063,Kernel-based geographically and temporally weighted autoregressive model for house price estimation,2018.0,PLoS ONE,J. Shim; Changha Hwang,"Spatiotemporal nonstationarity and autocorrelation are two crucial points in modeling geographical data. Previous studies have demonstrated that geographically and temporally weighted autoregressive (GTWAR) model accounts for both spatiotemporal nonstationarity and autocorrelation simultaneously to estimate house prices. Therefore, this paper proposes a kernel-based GTWAR (KBGTWAR) model by incorporating the basic principle of support vector machine regression into spatially and temporally varying coefficients model. The efficacy of KBGTWAR model is demonstrated through a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008. Comparing the existing models, KBGTWAR model obtains the lowest value for the residual sum of squares (RSS) and the highest value for the coefficient of determination R2. Moreover, KBGTWAR model improves the goodness of fit of the existing GTWAR model from 12.0 to 4.5 in terms of RSS, from 0.914 to 0.968 in terms of R2 and from 3.84 to 4.45 in terms of F-statistic. The results show that KBGTWAR model provides a comparatively high goodness of fit and sufficient explanatory power for both spatiotemporal nonstationarity and autocorre",19.0,https://doi.org/10.1371/journal.pone.0205063 455b3094212a104b5ab70dd047f6bbf3e6e6b475,10.1080/13658816.2024.2343771,A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan,2024.0,International Journal of Geographical Information Science,Jiale Ding; Wenying Cen; Sen-Sen Wu; Yi-Jun Chen; Jin Qi; Bo Huang; Zhen-Hong Du,"Abstract The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spati",19.0,https://figshare.com/articles/journal_contribution/A_neural_network_model_to_optimize_the_measure_of_spatial_proximity_in_geographically_weighted_regression_approach_a_case_study_on_house_price_in_Wuhan/25689685/1/files/45852016.pdf 09503bd0bba9d6200d9915f7844ce851f1339b64,10.1080/24694452.2024.2412173,Transferred Bias Uncovers the Balance Between the Development of Physical and Socioeconomic Environments of Cities,2024.0,Annals of the American Association of Geographers,Ce Hou; Fan Zhang; Yu-Hao Kang; Song Gao; Yong Li; Fábio Duarte; Sen Li,"Evaluating the balance between a city’s physical and socioeconomic environmental development is crucial for creating sustainable and livable urban spaces. Although they might appear contradictory, they jointly support the comprehensive sustainable urban development strategy. Traditional methods usually focus on assessing this balance from a specific perspective, such as how neighborhood greenery shapes real estate value. Yet, they fail to deliver a holistic balance assessment in developing the physical and socioeconomic dimensions. To fill this gap, this study introduces a research framework that measures this balance through house prices based on transferred bias. Using house price as an indicator shaped by both physical and socioeconomic environments, the framework first constructs a series of deep learning models to estimate house prices through street view images for each city. These models capture the relationship between neighborhood appearance and house price. Second, by leveraging transfer inference, we introduce neighborhood appearance from one city into the model trained from another city. This process identifies the transferred bias, which is the disparity between inaccu",19.0, 3460ad0578b3b8ba1a442a838ebf0e7c34942eb9,10.1080/10691898.2008.11889569,Modeling Home Prices Using Realtor Data,2008.0,,I. Pardoe,"It can be challenging when teaching regression concepts to find interesting real-life datasets that allow analyses that put all the concepts together in one large example. For example, concepts like interaction and predictor transformations are often illustrated through small-scale, unrealistic examples with just one or two predictor variables that make it difficult for students to appreciate how these concepts might be applied in more realistic multi-variable problems. This article addresses this challenge by describing a complete multiple linear regression analysis of home price data that covers many of the usual regression topics, including interaction and predictor transformations. The analysis also contains useful practical advice on model building—another topic that can be hard to illustrate realistically—and novel statistical graphics for interpreting regression model results. The analysis was motivated by the sale of a home by the author. The statistical ideas discussed range from those suitable for a second college statistics course to those typically found in more advanced linear regression courses.",19.0,https://doi.org/10.1080/10691898.2008.11889569 6780d7ed10512152f92b6800e6f8062c5d99f2ae,10.9734/ajrcos/2023/v16i2339,Machine Learning Approach for House Price Prediction,2023.0,Asian Journal of Research in Computer Science,M. J. Chowhaan; D. Nitish; G. Akash; N. Sreevidya; Subhani Shaik,"In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. To analyze the key elements and the best predictive models for home prices, literature research is conducted. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Our results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize for this research would both be greatly aided by this study, especially for housing developers and researchers.",19.0,https://journalajrcos.com/index.php/AJRCOS/article/download/339/675 5e4eb46e0fe34316820d6f29f559a88c59b7d9aa,10.22452/ijps.vol5no1.3,Fuzzy Logic Application for House Price Prediction,2015.0,International Journal of Property Sciences,A. G. Sarip; Muhammad Burhan Hafez,"Various methods have been used previously to estimate housing sales prices to model the underlying non-linearity relation between housing attributes and the price of property. More advanced non-linear modelling techniques such as Artificial Neural Networks (ANN), Fuzzy Inference System (FIS) and Fuzzy Logic (FL) emerged recently to model the nonlinear relation between the independent variables and the price function. A new structured model for house prices prediction based on Fuzzy Logic is proposed. A fuzzy logic based regression model has proved to be effective to address many prediction problems used in business forecasting, marketing and insurance. This paper highlights the development of a theoretical formulation for sales price prediction through the utilisation of a fuzzy regression model by applying fuzzy logic and fuzzy inference system techniques. The results show favourable outputs which indicate superior prediction function when compared with ANN and FIS as well as indicate the fuzzy functional relationship between dependent and independent variables.",19.0,https://ejournal.um.edu.my/index.php/IJPS/article/download/4368/2175 b14f7815b7a0280ab2775b5d243b2cc75a1ec3f6,10.1016/j.buildenv.2022.109577,Value capitalization effects of green buildings: A new insight through time trends and differences in various price levels,2022.0,Building and Environment,I-Chun Tsaim,"While many studies have explored green buildings, there remain many unsolved questions about how to expand this section of the housing market, such as how the capitalization effect of green buildings varies across time and location. This study investigates the price premiums of green buildings in Taipei City, the largest metropolis in Taiwan, to examine how they have changed over time, and assesses the magnitude of the capitalization effects under varying housing price levels. The empirical results indicate that Taipei residents are willing to pay higher prices for houses with green-label certification. The higher the certification level, the higher the price that residents are willing to pay. This study additionally identifies two characteristics of the capitalization effects in this market. The first is that the price premiums vary with the market condition. For example, during housing market downturns (e.g., 2014-2015), building green premiums may be unremarkable or non-existent. The second effect identified is that green buildings tend to exhibit an 'aristocratic trend.' This means that high-priced houses (i.e., houses priced above the 80% quantile) in the high-end label catego",19.0, 987c64347befe9e9d7df1e26d467ed4fdd8b9a85,10.1016/j.euroecorev.2019.08.002,Financial and fiscal interaction in the Euro Area crisis: This time was different,2018.0,European Economic Review,Alberto Caruso; L. Reichlin; G. Ricco,"This paper highlights the anomalous characteristics of the Euro Area `twin crises' by contrasting the aggregate macroeconomic dynamics in the period 2009-2013 with the business cycle fluctuations of the previous decades. We report three stylised facts. First, the contraction in output was marked by an anomalous downfall in investment, while consumption, savings and unemployment followed their historical relation with GDP. Second, households' and financial corporations' debts, and house prices deviated from their pre-crisis trends. Third, the jump in the public deficit-GDP ratio in 2008-2009 was unprecedented and so was the fiscal consolidation that followed. Our analysis points to the financial nature of the crisis as a likely explanation for these facts. Importantly, the `anomaly' in public deficit is in large part explained by extraordinary measures in support of the financial sector, which show up in the stock-flow adjustments and reveal a key interaction between the fiscal and the financial sectors.",19.0,https://www.sciencedirect.com/science/article/am/pii/S0014292119301357?via%3Dihub 0a91dd72511aff28a4f4061d0ebf65bfbeaf0914,10.2478/v10031-012-0036-3,The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe,2012.0,Aestimum,M. Renigier-Biłozor; R. Wiśniewski,"Abstract This paper aims to determine the influence of selected variables on residential property price indices for the European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented. Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects 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 of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.",19.0,https://sciendo.com/pdf/10.2478/v10031-012-0036-3 17dfad4bdda2ef9eab5e834430935e4b47a3175c,10.1590/s0101-47142005000100003,Tecido urbano e mercado imobiliário em São Paulo: metodologia de estudo com base na Décima Urbana de 1809,2005.0,,B. P. S. Bueno,"This article presents a new spatialisation methodology for the Decima Urbana, the first property tax established in Brazilian cities. The case of Sao Paulo in 1809 is studied. The data gathered from textual documentation was processed in a database and cartographed on the first official city plan, elaborated by engineer Carlos Bresser between 1844-1847, and then confronted with the iconographic documentation produced by visiting travellers and by photographer Militao Augusto de Azevedo, so as to cross-reference the information obtained. The Decima Urbana of 1809 contains data about the siting of buildings, their proprietors, tenants, typology (single and two-storey houses, shops), finalities (own use, rent), uses (residential, commercial, mixed) and value. This allows for the present day hypothetical reconstruction of old Sao Paulo's urban mesh and of aspects of the real estate market dynamics at the end of the colonial period.",19.0,https://doi.org/10.1590/s0101-47142005000100003 d17926cec2692825ef0e6ea81e1f6aa3924ab181,10.5089/9781455201785.001,Local Housing Market Cycle and Loss Given Default: Evidence from Sub-Prime Residential Mortgages,2010.0,Social Science Research Network,Yanan Zhang; Lu Ji; Fei Liu,"This paper studies the impact of housing market cycles on loss given default (LGD). Previous studies have shown that the current loan-to-value ratio (CLTV) is the most important determinant of LGD. This paper establishes another linkage which is between the house price cycles before the time of mortgage origination and LGD. The empirical analysis is based on a large loan-level sub-prime residential mortgage loss dataset from 1998 to 2009. Results show that house price history has a long memory in explaining LGD. Its explanatory power far exceeds the original LTV and other loan characteristics. This paper offers a countercyclical view of LGD risk. The model can be combined with a default probability model to serve as a regulatory prudential tool. Such a tool provides a solution to the inherent procyclical bias in BASEL II capital requirements, and can contribute to the safety and soundness of banking institutions.",19.0,https://www.elibrary.imf.org/doc/IMF001/11145-9781455201785/11145-9781455201785/Other_formats/Source_PDF/11145-9781455201501.pdf afac39996859dec0e93402bafd2f8d62d316c2bf,10.1016/j.ufug.2023.128060,Evaluating Simulated Visible Greenness in Urban Landscapes: An Examination of A Midsize U.S. City,2023.0,Urban Forestry & Urban Greening,Jin-ming Yan; Reza Naghedi; Xiao Huang; Si-Qin Wang; Jun-Yu Lu; Yang Xu,"Urban greenness is critical in evaluating urban environmental and living conditions, significantly affecting human well-being and house prices. Unfortunately, satellite imagery from a bird-eye view does not fully capture urban greenness from a human-centered perspective, while human-perceived greenness from street-view images heavily relies on road networks and vehicle accessibility. In recent years, scholars started to explore greenness measurements from a simulative perspective, among which the simulation of the Viewshed Greenness Visibility Index (VGVI) received wide attention. However, the simulated VGVI lacks a comprehensive assessment. To fill this gap, we designed a field experiment in Fayetteville, Arkansas, by collecting 360-degree panoramas in different local climate zones. Further, we segmented these panoramas via the state-of-the-art DeeplabV2 neural network to obtain the Panoramic Greenness Visibility Index (PGVI), which served as the ground-truthing human-perceived greenness. We assessed the performance of VGVI by comparing it with PGVI calculated from field-collected panoramas. The results showed that, despite the disparity of performance in different local climate z",19.0, d1f212ca2f8fa11f4a26604ead5a0f784326e48e,10.2849/694,Pockets of Risk in European Housing Markets: Then and Now,2019.0,Social Science Research Network,Jane Kelly; Julia Le Blanc; R. Lydon,"Using household survey data, we document evidence of a loosening of credit standards in Euro area countries that experienced a property price boom-and-bust cycle. Borrowers in these countries exhibited significantly higher loan-to-value (LTV) and loan-to-income (LTI) ratios in the run up to the financial crisis, and an increasing tendency towards longer-term loans compared to borrowers in other countries. In recent years, despite the long period of historically low interest rates and substantial house price increases in some countries, we do not find similar credit easing as before the crisis. Instead, we find evidence of a considerable change in borrower characteristics since 2010: new borrowers are older and have higher incomes than before the crisis. JEL Classification: E5, G01, G17, G28, R39",19.0, 668d07bf051b66c27a6176fb59b2559a1aade831,10.3923/sscience.2010.559.564,Does the Functional Form Matter in the Estimation of Hedonic Price Model for Housing Market,2010.0,,A. K. Bello; Alabi Moruf,,19.0,http://docsdrive.com/pdfs/medwelljournals/sscience/2010/559-564.pdf f68b93fc5eb9b42ba1bfa8ed232491ecaf7b5c15,10.47324/ilkominfo.v4i2.129,Sistem Pendukung Keputusan Pemilihan Perumahan Dengan Menggunakan Metode Multi Attribute Utility Theory,2021.0,Jurnal Ilmiah Ilkominfo,Muhammad L. O. Mardin; Achamad Fuad; Hairil Kurniadi Sirajuddin,"Abstrak: Banyaknya pilihan rumah seringkali membuat calon pembeli merasa ragu atau kesulitan saat harus menentukan langsung rumah yang mana yang akan dibeli, karena pada pemilihan perumahan yang akan dibeli belum ada sistem yang akan membantu dalam memilih perumahan yang dibeli, sehingga pada proses pemilihan masih menggunakan pikiran saja dan belum ada perhitungan pada saat pemilihan perumahan yang akan di beli. Tujuan penelitian ini menghasilkan sebuah sistem pendukung keputusan pemilihan perumahan. Kriteria yang diajukan dalam proses pemilihan perumahan yaitu: Harga perumahan, Jarak dari pusat kota, Jarak dengan pasar terdekat, [1], tipe perumahan, jarak dengan jalan umum, jarak dengan lahar. Dari hasil pemilihan perumahan menggunakan sistem yang telah dibuat. dengan 10 alternatif, dengan tingkat kepentingan masing-masing kriteria yang digunakan yaitu: harga = 5, tipe rumah = 5, jarak dengan pusat kota = 2, jarak dengan pasar terdekat = 2, jarak dengan jalan umum = 4, jarak perumahan dengan lahar = 5, telah diperoleh alternatif yang akan direkomendasikan yaitu perumahan safira residen 70 dengan dengan nilai tertinggi 0,65.Kata kunci: Sistem Pendukung Keputusan, Pemilihan, Peruma",18.0,http://www.j-ilkominfo.org/index.php/ejournalaikom/article/download/129/48 c7ee9a6b0834ce714d6b62db88a2133ebe29f97e,10.3390/ijgi6120380,"Neighborhood Characteristics, Alcohol Outlet Density, and Alcohol-Related Calls-for-Service: A Spatiotemporal Analysis in a Wet Drinking Country",2017.0,ISPRS Int. J. Geo Inf.,Miriam Marco; B. Freisthler; E. Gracia; A. López-Quílez; M. Lila,"Alcohol outlets have been associated with different social problems, such as crime, violence, intimate partner violence, and child maltreatment. The spatial analysis of neighborhood availability of alcohol outlets is key for better understanding of these influences. Most studies on the spatial distribution of alcohol outlets in the community have been conducted in U.S. cities, but few studies have assessed this spatial distribution in other countries where the drinking culture may differ. The aim of this study was to analyze the spatiotemporal distribution of alcohol outlets in the city of Valencia, Spain, and its relationship with neighborhood-level characteristics, as well as to examine the influence of alcohol outlet density on alcohol-related police calls-for-service. Spain is characterized by having a “wet” drinking culture and greater social acceptance of drinking compared to the U.S. Data on alcohol outlets between 2010–2015 in three categories (off-premise, restaurants and cafes, and bars) were used for the analysis. We used the 552 census block groups allocated within the city as neighborhood unit. Data were analyzed using Bayesian spatiotemporal regression models. Results",18.0,https://www.mdpi.com/2220-9964/6/12/380/pdf?version=1511442934 c028299e71a71ee7ad858d99cfea2cd912973b8f,10.18564/jasss.3071,A Simple Agent-Based Spatial Model of the Economy: Tools for Policy,2015.0,Journal of Artificial Societies and Social Simulation,Bernardo Alves Furtado; I. D. R. Eberhardt,"This study simulates the evolution of artificial economies in order to understand the tax relevance of administrative boundaries in the quality of life of its citizens. The modeling involves the construction of a computational algorithm, which includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The real estate market allows families to move to dwellings with higher quality or lower price when the families capitalize property values. The goods market allows consumers to search on a flexible number of firms choosing by price and proximity. The labor market entails a matching process between firms (location) and candidates (qualification). The government may be configured into one, four or seven distinct sub-national governments. The role of government is to collect taxes on the value added of firms in its territory and invest the taxes into higher levels of quality of life for residents. The model does not have a credit market. The results suggest that the configuration of administrative boundaries is relevant to the levels of quality of life arising from the reversal of taxes. The model with seven regi",18.0,http://jasss.soc.surrey.ac.uk/19/4/12/12.pdf 6ff77fee24ef0cbc227ae704f23ca49f19d1e1db,10.1111/tbj.13849,Impact of socioeconomic status on psychological functioning in survivorship following breast cancer and reconstruction,2020.0,The Breast Journal,Nicole K. Le; Kyle S. Gabrick; Fouad Chouairi; E. Mets; Tomer Avraham; M. Alperovich,"Socioeconomic status (SES) remains an important population health risk factor and impacts a patient's experience of care during breast cancer. This study explored the relationship between SES and quality of life and satisfaction in survivorship following breast cancer and reconstruction. All patients underwent breast reconstruction at a single academic center from 2013 to 2017. Patients completed the five quality of life and satisfaction domains of the BREAST‐Q, a validated patient‐reported outcome measure. Estimated home value using a web‐based real estate website was used to approximate a patient's socioeconomic status. Correlations were evaluated using Pearson's correlation methods, where appropriate, as well as analysis of covariance. Data were stratified for comparison utilizing t tests and linear regression models. Significance was defined as P ≤ .05. Four hundred patients underwent 711 breast reconstructions during the study time period. Satisfaction with the breast (P = .038) and psychosocial well‐being (P = .012) had significant positive correlations with increasing socioeconomic status. When stratifying patients’ socioeconomic status into thirds, the upper third had signi",18.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/tbj.13849 dd16c23c43cc7c0b96bd898f384d266c734a5526,10.3390/ijerph18063315,Associations between Greenspace and Gentrification-Related Sociodemographic and Housing Cost Changes in Major Metropolitan Areas across the United States,2021.0,International Journal of Environmental Research and Public Health,Leah H. Schinasi; Helen V. S. Cole; J. Hirsch; G. Hamra; P. Gullón; Felicia Bayer; S. Melly; K. Neckerman; J. Clougherty; G. Lovasi,"Neighborhood greenspace may attract new residents and lead to sociodemographic or housing cost changes. We estimated relationships between greenspace and gentrification-related changes in the 43 largest metropolitan statistical areas (MSAs) of the United States (US). We used the US National Land Cover and Brown University Longitudinal Tracts databases, as well as spatial lag models, to estimate census tract-level associations between percentage greenspace (years 1990, 2000) and subsequent changes (1990–2000, 2000–2010) in percentage college-educated, percentage working professional jobs, race/ethnic composition, household income, percentage living in poverty, household rent, and home value. We also investigated effect modification by racial/ethnic composition. We ran models for each MSA and time period and used random-effects meta-analyses to derive summary estimates for each period. Estimates were modest in magnitude and heterogeneous across MSAs. After adjusting for census-tract level population density in 1990, compared to tracts with low percentage greenspace in 1992 (defined as ≤50th percentile of the MSA-specific distribution in 1992), those with high percentage greenspace (d",18.0,https://www.mdpi.com/1660-4601/18/6/3315/pdf?version=1616501987 73a4d1649cc2f4a13a13cf54b8a118d823a143ab,10.3390/land13111934,Identification of Urban Renewal Potential Areas and Analysis of Influential Factors from the Perspective of Vitality Enhancement: A Case Study of Harbin City’s Core Area,2024.0,Land,Xi-Quan Zhang; Lizhu Du; Xiaoyun Song,"In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution o",18.0,https://doi.org/10.3390/land13111934 8550fbc72c6b46622fd732c58d24972f4ea40c81,10.3390/land11010004,Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective,2021.0,Land,Hang Shen; Lin Li; Haihong Zhu; Yu Liu; Zhenwei Luo,"Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the lo",18.0,https://www.mdpi.com/2073-445X/11/1/4/pdf?version=1640083411 813aea7b2a842cc8a747542ce36fc12cd0919b8c,10.1007/s41685-022-00262-7,Impacts of the COVID-19 pandemic on private rental housing prices in Turkey,2022.0,Asia-Pacific Journal of Regional Science,Safiye Özge Subaşı; T. Baycan,"Rent prices have a strong relationship with economic factors in addition to the structural and environmental characteristics of housing stocks. Previous research demonstrated that impacts of unexpected and sudden circumstances such as war and epidemics on urban housing markets relate to their effects on the economy. Following the first COVID-19 case in Turkey, which was officially announced on 11 March 2020, changes in both housing preferences and economic structure have significantly affected the rental housing market due to the pandemic conditions. To highlight challenges in the rental housing market, this study addressed how the COVID-19 pandemic has influenced rental housing prices in 81 provinces of Turkey using the big data set of Endeksa, a private real-estate platform in Turkey. The data set was descriptively analyzed through four main periods identified on the basis of changing COVID-19 pandemic regulations and implementations in Turkey. Average rent prices of Turkish provinces during the identified periods were compared using ArcGIS 10.6. to show how private rent prices changed during the pandemic. The findings demonstrated that the unit rent prices generally increased fr",18.0,https://link.springer.com/content/pdf/10.1007/s41685-022-00262-7.pdf 69b13c605d7bd1379b0010e4a1d1ac7422ff6c8e,10.3390/math13152526,Interpretable Machine Learning Framework for Corporate Financialization Prediction: A SHAP-Based Analysis of High-Dimensional Data,2025.0,Mathematics,Yanhe Wang; Wei Wei; Zhuo-Dong Liu; Jia-He Liu; Yinzhen Lv; Xiang-Yu Li,"High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R2 of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_S",18.0,https://www.mdpi.com/2227-7390/13/15/2526/pdf?version=1754484777 deecbed170b4346f9c6b5d21511304ae3e383268,10.1016/j.pmedr.2016.05.008,The impact of area residential property values on self-rated health: A cross-sectional comparative study of Seattle and Paris,2016.0,Preventive medicine reports,J. Jiao; A. Drewnowski; A. Moudon; A. Aggarwal; J. Oppert; H. Charreire; B. Chaix,"This study analyzed the impact of area residential property values, an objective measure of socioeconomic status (SES), on self-rated health (SRH) in Seattle, Washington and Paris, France. This study brings forth a valuable comparison of SRH between cities that have contrasting urban forms, population compositions, residential segregation, food systems and transportation modes. The SOS (Seattle Obesity Study) was based on a representative sample of 1394 adult residents of Seattle and King County in the United States. The RECORD Study (Residential Environment and Coronary Heart Disease) was based on 7131 adult residents of Paris and its suburbs in France. Socio-demographics, SRH and body weights were obtained from telephone surveys (SOS) and in-person interviews (RECORD). All home addresses were geocoded using ArcGIS 9.3.1 (ESRI, Redlands, CA). Residential property values were obtained from tax records (Seattle) and from real estate sales (Paris). Binary logistic regression models were used to test the associations among demographic and SES variables and SRH. Higher area property values significantly associated with better SRH, adjusting for age, gender, individual education, income",18.0,https://www.sciencedirect.com/science/article/pii/S2211335516300389/pdf 5d6a24c5fc5936c76afccb25ba046986475b50c4,10.3390/buildings14123823,Sustainability Certifications in Real Estate: Value and Perception,2024.0,Buildings,A. Marques; João Fragoso Januário; Carlos Oliveira Cruz,"This study examines the influence of sustainability certifications on the real estate market, particularly highlighting the advantages they offer compared to uncertified buildings and their recognition within the industry. A survey targeting various industry professionals garnered ninety responses, predominantly from the real estate sector. The survey explored the respondents’ awareness and perceived benefits of sustainability certifications, their priority areas within sustainability, and the relevance of these certifications across different real estate sectors. The analysis also compared the additional costs and operational savings of certified versus uncertified buildings. Among the certifications, LEED and BREEAM were the most recognized. The primary benefits associated with these certifications included enhanced corporate image, improved health and well-being, increased building value, and higher rental yields. We estimated a valuation and rent premium for certified buildings, noting that these premiums were more pronounced among respondents who were younger, had less professional experience, and were from the property sector. The office market was identified as the segment p",18.0,https://www.mdpi.com/2075-5309/14/12/3823/pdf?version=1732871204 49795d686dbc88a9699003c9e105269544ba458f,10.3390/ijerph18030916,Housing and Adult Health: Evidence from Chinese General Social Survey (CGSS),2021.0,International Journal of Environmental Research and Public Health,Nanqian Chen; Yangyang Shen; Hai-Lun Liang; Rui Guo,"Housing is one of the social determinants of health and the most basic survival needs of human beings. Many studies have preliminarily confirmed that housing factors can influence residents’ health. The aims of this study were: to evaluate the housing factors associated with self-rated health and mental health among Chinese residents; to explore the regional heterogeneity of the impact of housing on health; and to assess the effects of housing on health among different age groups. Data was derived from the Chinese General Social Survey (CGSS). Housing factors were analyzed along six dimensions: housing property, living space, number of living people, number of houses, living region and housing price. Self-rated health and mental health were used to measure health outcomes. Multivariate ordered logistic regression was performed to assess the relationship between housing and health. The living space, living region and housing price was significantly associated with self-rated health. The number of living people living region and housing price were related to mental health. The influence of housing factors on health were more pronounced among residents living in eastern and central ar",18.0,https://www.mdpi.com/1660-4601/18/3/916/pdf?version=1611287831 d489781f49c7c107af16c21bd0279d71e4d41a4e,10.3390/su13042236,The Impacts in Real Estate of Landscape Values: Evidence from Tuscany (Italy),2021.0,Sustainability,F. Riccioli; R. Fratini; Fabio Boncinelli,"Using spatial econometric techniques and local spatial statistics, this study explores the relationships between the real estate values in Tuscany with the individual perception of satisfaction by landscape types. The analysis includes the usual territorial variables such as proximity to urban centres and roads. The landscape values are measured through a sample of respondents who expressed their aesthetic-visual perceptions of different types of land use. Results from a multivariate local Geary highlight that house prices are not spatial independent and that between the variables included in the analysis there is mainly a positive correlation. Specifically, the findings demonstrate a significant spatial dependence in real estate prices. The aesthetic values influence the real estate price throughout more a spatial indirect effect rather than the direct effect. Practically, house prices in specific areas are more influenced by aspects such as proximity to essential services. The results seem to show to live close to highly aesthetic environments not in these environments. The results relating to the distance from the main roads, however, seem counterintuitive. This result probably ",18.0,https://www.mdpi.com/2071-1050/13/4/2236/pdf?version=1613809472 cf103c1ff77ecff33cc3dcde7cfa30d40e2c90e8,10.1108/ijhma-11-2020-0133,"Factors affecting housing prices in Izmir, Turkey: a quantile regression approach",2021.0,,Onur Özsoy; H. Sahin," Purpose The purpose of this paper is to investigate empirically the main factors that affect the house prices in Izmir, Turkey using the quantile regression and ordinary least square approaches. Design/methodology/approach Sample data about the housing market for Izmir collected from the web pages of various real estate agencies during June 2018. Following this, the quantile regression method is used to estimate all possible effects of variables on each interested quantile to determine the factors that affect house prices to guide the potential consumers, house developers, city planners and the policymakers in Izmir, Turkey. Findings Results show that the age of the house, central heating and parking have no significant effect on prices. The size of the house, the existence of an elevator, fire and security have a positive and significant effect on prices. The number of rooms has lower values for high-priced houses, while the floor, the number of balconies, air conditioning, proximity to schools have a higher value for high-priced houses. The number of toilets, the number of bathrooms and the distance to the hospital have a lower value on the high-priced housing. The value of ",18.0, 79aa353cf47cf4a81a3da338a56f2275edaa63b2,10.1177/0042098017705829,Municipality attraction and commuter mobility in urban Sweden: An analysis based on longitudinal population data,2018.0,,S. Schéele; G. Andersson,"At the individual level, commuting can be seen as part of a search process that may lead to adjustments in terms of migration or change of workplace. The behaviour of commuters is affected by individual characteristics and factors related to housing, labour and transport markets. It can provide insight into factors related to different municipalities’ levels of attraction. In our study, we provide a longitudinal analysis of individual commuting behaviour during a one-year study period: we simultaneously address the dynamics of ending commuting by a migration event, a change of workplace, or both. Our study is situated in the urban region that surrounds lake Mälaren of Sweden, including its capital Stockholm. We draw on unique register data on the entire commuter population of that region and linked contextual data on the characteristics of the municipalities where the commuters live and work. Migration rates are strongly related to demographic variables, whereas the propensity to change workplace mainly varies with economic variables. We demonstrate that the attraction of a municipality in terms of residence increases with the general accessibility to workplaces and decreases with ",18.0,https://doi.org/10.1177/0042098017705829 cd1fec90492e585de693209af0d307109ff80290,10.1016/j.regsciurbeco.2017.10.009,The Impact of the Cost of Car Ownership on the House Price Gradient in Singapore,2018.0,,Naqun Huang; Jing Li; Amanda Ross,"Abstract This paper examines the extent to which a change in the cost of car ownership affects the house price gradient with respect to distance from the central business district (CBD). Theory suggests that if the cost of owning a car increases, people will use other modes of transportation, thus reducing house prices farther away from the CBD and increasing house prices closer to the city center. However, in an empirical setting, the cost of car ownership is likely to be endogenous and correlated with various unobserved factors that also contribute to changes in the house price gradient. To obtain causal effects, we exploit a unique feature of Singapore's car registration process. All cars in Singapore must have a Certificate of Entitlement (COE), but the number available is restricted based on the traffic concerns of the government and are allocated through a competitive bidding process. We use the number of COEs available each quarter as an instrument for the price of a COE, as the quota is likely to be correlated with the price of the COE but not the price of housing at various distances from the CBD. We find that when the price of a COE increases, the price of housing closer ",18.0,https://ink.library.smu.edu.sg/context/soe_research/article/3023/viewcontent/Huang_Li_Ross.pdf ac6152c2a324e9ff5b7ca260ee98a468d126ad61,10.3390/en14133802,Improvements and Spatial Dependencies in Energy Transition Measures,2021.0,Energies,M. Kuc-Czarnecka; M. Olczyk; M. Zinecker,"This article aims to improve one of the newest energy transition measures—the World Economic Forum WEF Energy Transition Index (ETI) and find its driving forces. This paper proposes a new approach to correct the ETI structure, i.e., sensitivity analysis, which allows assessing the accuracy of variable weights. Moreover, the novelty of the paper is the use the spatial error models to estimate determinants of the energy transition on different continents. The results show that ETI is unbalanced and includes many variables of marginal importance for the shape of the final ranking. The variables with the highest weights in ETI did not turn out to be its most important determinants, which means that they differentiate the analysed countries well; nonetheless, they do not have sufficient properties of approximating the values of the ETI components. The most important components of ETI (with the highest information load) belong to the CO2 emissions per capita, the innovative business environment, household electricity prices, or renewable capacity buildout. Moreover, we identified the clustering of both ETI and its two main pillars in Europe, which is not observed in America and Asia. The",18.0,https://www.mdpi.com/1996-1073/14/13/3802/pdf?version=1624532768 3a8000c6105bec9fa2fce0f03b88ceb8d0593564,10.1177/23998083221140415,How do urban services facilities affect social segregation among people of different economic levels? A case study of Shenzhen city,2022.0,Environment and Planning B Urban Analytics and City Science,Yuyang Wu; Yao Yao; Shu-Liang Ren; Shi-Yi Zhang; Qing-Feng Guan,"Social segregation hinders the development of cities and has become a hot topic in urban research. Existing studies have focused on the difference in the distribution of crowd activities to measure segregation but have ignored the impact of the urban environment on crowd gathering and segregation. To study the impact and understand social segregation more comprehensively, we coupled mobile phone datasets and housing price data to divide city dwellers into three socioeconomic levels. Considering that spatial colocation is a necessary condition for interaction among various social groups, spatial colocation probability was proposed to quantitatively describe the degree of social segregation at the community scale. Point-of-interest (POI) data were introduced to represent the urban service facilities. The effect of urban service facilities on the segregation of different groups was analyzed by using geographically weighted regression (GWR). The results indicate three points, as follows. (1) Significant social segregation in Shenzhen mostly occurs in suburban and downtown areas, and the interaction segregation of people mainly occurs between people with high and low socioeconomic level",18.0, 1b27ce63eb206f91ede85a8cc238ddbeead94b29,10.2478/remav-2020-0003,The Impact of a Mass Rapid Transit System on Neighborhood Housing Prices: An Application of Difference-In-Difference and Spatial Econometrics,2020.0,,Chun-Chang Lee; Chih-Min Liang; Hui-Chuan Hong,"Abstract This study discusses the impact of the mass rapid transit (MRT) system’s construction and operation on neighborhood housing prices. Estimations were conducted by considering the spatial dependence of housing prices and using a difference-in-difference (DD) method that incorporated the spatial lag model and spatial error model. The empirical results indicated that the coefficient of the interaction variable of the MRT’s range of influence and commencement time of construction was -0.079, reaching the 10% significance level. Hence, when MRT construction began, housing prices in the experimental group decreased by 7.9% as compared to the control group. A possible reason for these findings is that the “dark age” of urban traffic, air pollution, and noise brought about by MRT construction negatively affected housing prices. With regard to MRT operations, the coefficient of the interaction variable of the MRT’s influence range and service commencement time did not reach the level of significance, indicating that housing prices within the MRT’s influence range were not significantly affected when the MRT’s service began. This might be due to the fact that the psychological expect",18.0,https://www.sciendo.com/pdf/10.2478/remav-2020-0003 efb27fc3d0471d743e06f448aea63a2939af1203,10.3390/su10030642,Effects of Bank Lending on Urban Housing Prices for Sustainable Development: A Panel Analysis of Chinese Cities,2018.0,,Yong-Sheng Jiang; Dong Zhao; Andrew Sanderford; Jing Du,"Stable home prices are critical to a healthy housing market and sustainable development. Home prices in many Chinese cities have increased dramatically in past decades. The China’s central bank uses two primary monetary controls on overheated housing prices: the bank lending supply and lending rate. However, little to no evidence informs whether the nation-level bank lending controls are effective at the city level. Unlike extant studies that only focus on nation-level effects of such controls, this research analyzes long-run effects on housing prices at the national, regional, and city levels. The authors perform cross-sectional time-series regressions on empirical data from 35 major Chinese cities for the period 2003 to 2015. Results confirm that controlling lending rates is effective as a long-term measure at the national, regional, and city levels, whereas controlling the lending supply is effective as a short-term measure for many cities. Results also reveal that housing prices cause lending supply changes for many regions in a long run and indicate that credit policy often lags in response to housing price changes. Findings show that the effectiveness of bank lending largely ",18.0,https://www.mdpi.com/2071-1050/10/3/642/pdf?version=1519819471 bb63c63917e51e1931ae23ee6ef45b634acc07e2,10.1080/10095020.2018.1503775,Road distance and travel time for an improved house price Kriging predictor,2018.0,Geo-Spatial Information Science,H. Crosby; T. Damoulas; Alex Caton; Paul Davis; João Porto de Albuquerque; S. Jarvis,"ABSTRACT The paper designs an automated valuation model to predict the price of residential property in Coventry, United Kingdom, and achieves this by means of geostatistical Kriging, a popularly employed distance-based learning method. Unlike traditional applications of distance-based learning, this papers implements non-Euclidean distance metrics by approximating road distance, travel time and a linear combination of both, which this paper hypothesizes to be more related to house prices than straight-line (Euclidean) distance. Given that – to undertake Kriging – a valid variogram must be produced, this paper exploits the conforming properties of the Minkowski distance function to approximate a road distance and travel time metric. A least squares approach is put forth for variogram parameter selection and an ordinary Kriging predictor is implemented for interpolation. The predictor is then validated with 10-fold cross-validation and a spatially aware checkerboard hold out method against the almost exclusively employed, Euclidean metric. Given a comparison of results for each distance metric, this paper witnesses a goodness of fit () result of 0.6901 ± 0.18 SD for real estate pric",18.0,https://www.tandfonline.com/doi/pdf/10.1080/10095020.2018.1503775?needAccess=true ed42fa40fbc0a004411002765140cc1b55117d56,10.36253/aestim-13703,House price prediction modeling using machine learning techniques: a comparative study,2023.0,Aestimum,Ayten Yağmur; M. Kayakuş; Mustafa Terzioğlu,"In the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variables, by considering the features of the house. In this study, the price of the house was attempted to be predicted using machine learning methods by establishing a model with micro variables that reveal the features of the house. The study was conducted in Turkey’ Antalya province, where household housing demand of foreigners is also high. The house advertisements in locations belonging to the lower, middle- and upper-income groups were selected as the sample. In the results, it was observed that the artificial neural network (ANN) method made predictions with more meaningful results compared to support vector regression (SVR) and multiple linear regression (MLR). These results appear to be a viable model for institutions that supply housing, mediate housing sales, and provide housing financing and valuation. It is considered that this model, which can be used to predict fluctuating house ",18.0,https://oaj.fupress.net/index.php/ceset/article/download/13703/11696 d2d3026249adf766dde1c32ffa102aa35fc8f5df,10.4236/oalib.1109176,Investigating Social Factors of Residential Satisfaction and the Impact on Housing Price in Spontaneous Settlements in Tehran Fringe,2022.0,OALib,R. Ahmadi; Sheida Ghahremani; Sepideh Besharati Kivi; F. Bayat; Nafiseh Zareh; Amirreza Rohani; Rashin Hamidi; Negin Hamidi; Kaveh Ghamisi; Parisa Janianpour,"Evaluating the degree of residential satisfaction with spontaneous settlements entails a large number of factors. The present study is conducted aiming to specify social variables affecting RS in spontaneous settlements and the impact on housing prices. To do so, structural modeling and regression methods were utilized to assess research hypotheses. The survey was conducted on 200 respondents residing in Qaemiyeh residence located around Tehran. The results of structural modeling confirmed that a sense of belonging, a sense of security, neighbors' conformity and social interactions have respectively manifested specific effects on residential satisfaction (RS). Moreover, the factor of participation in local society has not shown a significant index of effectiveness on RS, nullifying the relative hypothesis. These findings imply that the formation of the appropriate social environment along with proper social interactions among residents can compensate for the physical shortcomings of the place of residence, leading to creating RS. The results also clarified that residents' satisfaction with the social constituents of their residence had explained 12.8% of the housing price soaring i",18.0,https://doi.org/10.4236/oalib.1109176 f6e066d10d0fef1481900621dddaf23a37f8179e,10.1155/2021/3744320,Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model,2021.0,Mathematical Problems in Engineering,Xiang Wang; Shen Gao; Shiyu Zhou; Yibin Guo; Yonghui Duan; Daqing Wu,"Aiming at the shortcomings of a single machine learning model with low model prediction accuracy and insufficient generalization ability in house price index prediction, a whale algorithm optimized support vector regression model based on bagging ensemble learning method is proposed. Firstly, gray correlation analysis is used to obtain the main influencing factors of house prices, and the segmentation forecasting method is used to divide the data set and forecast the house prices in the coming year using the data of the past ten years. Secondly, the whale optimization algorithm is used to find the optimal parameters of the penalty factor and kernel function in the SVR model, and then, the WOA-SVR model is established. Finally, in order to further improve the model generalization capability, a bagging integration strategy is used to further integrate and optimize the WOA-SVR model. The experiments are conducted to forecast the house price indices of four regions, Beijing, Shanghai, Tianjin, and Chongqing, respectively, and the results show that the prediction accuracy of the proposed integrated model is better than the comparison model in all cases.",18.0,https://downloads.hindawi.com/journals/mpe/2021/3744320.pdf 37efd6830edec0055742821d2615a05c3a9e1e0f,10.2139/ssrn.3422650,Financial Cycles Across G7 Economies: A View from Wavelet Analysis,2019.0,Social Science Research Network,Martin Mandler; M. Scharnagl,"We analyse the cross-country dimension of financial cycles by studying cyclical co-movements in credit, house prices, equity prices and interest rates across the G7 economies. We use wavelet-based statistics to assess at which frequencies cyclical fluctuations and their crosscountry co-movements are important and how these change over time. We show cycles in interest rates and equity prices to be at least as synchronised as cycles in real GDP while cycles in credit and house prices are less synchronised. As a result, cross-country common cycles in equity prices and long-term interest rates account for a larger share of the volatility of these variables at the country level than common cycles in credit aggregates and house prices. A cluster analysis shows a high degree of similarity in the spectral characteristics of cycles in interest rates and equity prices across all countries but less similarities for cycles in credit and house price. For credit and house price cycles country-specific developments turn out to be more important than the common cross-country cycles.",18.0,https://doi.org/10.2139/ssrn.3422650 94f8fa5d8528a8fb875d1dd4e35e1c4005228047,10.1002/hec.4018,Determinants of nursing home choice: Does reported quality matter?,2020.0,Health Economics,H. Schmitz; Magdalena A. Stroka-Wetsch,"Quality report cards addressing information asymmetry in the health care market have become a popular strategy used by policymakers to improve the quality of care for older people. Using individual level data from the largest German sickness fund merged with institutional level data, we examine the relationship between reported nursing home quality, as measured by recently introduced report cards, nursing home prices, nursing home's location, and the individual choice of nursing homes. Report cards were stepwise introduced as of 2009, and we use a sample of 2010 that includes both homes that had been evaluated at that time and that had not yet been. Thus, we can distinguish between institutions with above and below average ratings as well as nonrated nursing homes. We find that the probability of choosing a nursing home decreases in distance and price. However, we find no economically significant effect of reported quality on individuals' choice of nursing homes.",18.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hec.4018 ebd8c894f170c5715dc0515760df0559a6f43dc9,10.5018/economics-ejournal.ja.2015-28,Urban House Prices: A Tale of 48 Cities,2015.0,,K. Kholodilin; Dirk Ulbricht,"Abstract In this paper, the authors construct a unique data set of Internet offer prices for flats in 48 large European cities across 24 countries. The data collected between January and May 2012 from 33 websites, are drawn from Internet advertisements of dwellings. Using the resulting sample of more than 1,000,000 announcements, the authors compute the quality-adjusted city-specific house prices. Based on this information, they investigate the determinants of the apartment prices. Four factors are found to be relevant for the dwelling price level using Bayesian Model Averaging: Population density, mortgage per capita, income inequality, and unemployment rate. The results are robust to applying two alternative estimation techniques: OLS and quantile regression. Based on the auhors´ estimation results they are able to identify cities where the prices are overvalued. This is a useful indication of a build-up of house price bubbles.",18.0,https://www.degruyter.com/document/doi/10.5018/economics-ejournal.ja.2015-28/pdf 80f35b33854990fddc9a0f407b519428d8535d17,10.3390/ijgi5010004,An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing,2016.0,ISPRS Int. J. Geo Inf.,Yi Yang; Jiping Liu; Shenghua Xu; Yangyang Zhao,"This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. Housing prices in Beijing are assessed to validate the feasibility of the proposed model. The COGWR model demonstrated a better goodness-of-fit than the GWR when housing price data were limited because a COGWR is able to effectively absorb no-price data with explanatory variables into its learning by considering spatial variations and nonstationarity that may introduce significant biases into housing prices. This result demonstrates that a semisupervised geographic weighted regression may be effectively used to predict housing prices.",18.0,https://www.mdpi.com/2220-9964/5/1/4/pdf?version=1452602180 73b42a52080d9c6506b8dfbfc6d5ad212ace3057,10.1016/j.jtrangeo.2017.08.004,“Waiting on the train”: The anticipatory (causal) effects of Crossrail in Ealing,2017.0,,Sam Comber; Daniel Arribas-Bel,"This paper estimates the willingness-to-pay for anticipated journey-time savings introduced by the Crossrail intervention in the London Borough of Ealing. Given Crossrail remains under construction, we estimate how the anticipated benefit of Crossrail's announcement enters the house price determination process. Anticipated journey-time savings should enter the home-buyer's pricing equation because these benefits are speculatively internalised even before the service becomes operational. Using a experimental method that accounts for the possibility of a spatial autoregressive process in housing values, we test the hypotheses that the announcement of a new commuter rail service generated a location premium, and that house price appreciation reflected proximity to Crossrail terminals. Our evidence suggests home-buyers significantly valued proximity to planned Crossrail terminals following the post-announcement period.",18.0,https://www.sciencedirect.com/science/article/pii/S0966692317300820/pdf 28aaa885c5ec66de6422ccfa40feb346081f9b17,10.14324/111.444/ucloe.000019,"A new attribute-linked residential property price dataset for England and Wales, 2011–2019",2021.0,UCL Open Environment,B. Chi; A. Dennett; Thomas Oléron-Evans; R. Morphet,"Current research on residential house price variation in the UK is limited by the lack of an open and comprehensive house price database that contains both transaction price alongside dwelling attributes such as size. This research outlines one approach which addresses this deficiency in England and Wales through combining transaction information from the official open Land Registry Price Paid Data (LR-PPD) and property size information from the official open Domestic Energy Performance Certificates (EPCs). A four-stage data linkage is created to generate a new linked dataset, representing 79% of the full market sales in the LR-PPD. This new linked dataset offers greater flexibility for the exploration of house price (£/m2) variation in England and Wales at different scales over postcode units between 2011 and 2019. Open access linkage codes will allow for future updates beyond 2019.",18.0,https://ucl.scienceopen.com/document_file/cd1d29cd-2201-48c1-a5ec-26f35d24f158/ScienceOpen/ucloe-02-019-correct.pdf 712779f2577785ba69e8f4310fe840b343c363a1,10.48550/arxiv.2403.14385,Estimating Causal Effects with Double Machine Learning - A Method Evaluation,2024.0,arXiv.org,Jonathan Fuhr; P. Berens; Dominik Papies,"The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of causal effects. In this paper, we review one of the most prominent methods -""double/debiased machine learning""(DML) - and empirically evaluate it by comparing its performance on simulated data relative to more traditional statistical methods, before applying it to real-world data. Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear confounding relationships. This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we demonstrate that the method continues to critically depend on standard assumptions about causal structure and identification. When estimating the effects of air pollution on housing prices in our application, we find that DML estimates are consistently larger than estimates of less flexible methods. From our overall results, we provide actionable ",18.0, c50f28cdad6e91a18fafb5fc714add81af4ff1db,10.15453/0191-5096.3493,Exploring Homeowner Opposition to Public Housing Developments,2010.0,The Journal of Sociology & Social Welfare,Joanna Duke,"This paper examines the beliefs and attitudes of homeowners in two receiving communities of public housing units. Opposition to housing mobility programs is generally attributed to fear of falling property values and increased crime rates. Given the spatial and redistributive nature of the programs, this paper proposes and explores space and liberty-based ideologies as causes of dormant opposition persisting beyond relocation. Survey data were collected from two neighborhoods where developments containing public housing were located. Results indicate that ideologies about space and liberty are important to understanding receiving community opposition as well as the extent to which members of the receiving community feel that public housing residents are part of their community.",18.0,https://scholarworks.wmich.edu/cgi/viewcontent.cgi?article=3493&context=jssw a72105684504e2e70a7e97bdff4c8ce8e553e6b5,10.5089/9781484348550.001,Stabilizing China's Housing Market,2018.0,Social Science Research Network,Richard Koss; Xinrui Shi,"The sharp rise of house prices in China’s Tier-1 cities has fostered a great deal of commentary about the possibility of bubbles forming there. However, China’s unique housing market characteristics make it difficult to assess the macroeconomic severity of bursting bubbles, even if they exist. These include the setting of land supply and prices by the government, among many others. The presence of overbuilt “ghost cities” greatly complicates the ability of traditional macroeconomic policies to address these concerns. This paper looks at proposals to shore up the mortgage underwriting and legal infrastructure to help China withstand the impact of falling prices, should this occur.",18.0,https://www.imf.org/-/media/Files/Publications/WP/2018/wp1889.ashx 550052797342bda3b6aef186620699386fd19e8e,10.1108/jfmpc-08-2022-0041,Office property price index forecasting using neural networks,2023.0,Journal of Financial Management of Property and Construction,Xiaojie Xu; Yun Zhang," Purpose The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021. Design/methodology/approach The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios. Findings The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases. Originality/value The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis. ",18.0, a08e95cdab91ae1acf2fc06d72a1dad8577ad56a,10.1177/00420980211026555,Asymmetric housing information diffusions in China: An investor perspective,2021.0,Urban studies,Shu-hen Chiang; E. Hui; Chien-Fu Chen,"Over the past few decades, numerous attempts have been made to examine ripple effects using housing prices. What seems to be lacking, however, is a return to investor behaviour in terms of how it inspires inter-city spillovers. We thus propose the price-to-rent (P/R) ratio as a quantitative anchor in regard to how investor sentiment affects future housing values. By utilising a time-varying spillover approach based on monthly housing prices and rents across first-tier cities in China, it becomes clear that the characteristics of investment-driven diffusions are short-lived and more sensitive to economic policy changes in 2014 (the new normal initiative) and 2018 (strict housing control measures). Finally, in addition to good and bad perspectives, there is asymmetric evidence to show that negative outlooks such as a great fear of loss generally play a dominant role in the information transmission process, while a strong repercussion of good news in 2019 has subsequently been dampened by the COVID-19 pandemic.",18.0, 3dc8dfa7d26bd111784d8b6447f211ea53ecd397,10.1080/17421772.2018.1506150,Estimation and testing for a partially linear single-index spatial regression model,2018.0,Spatial Economic Analysis,Yan Sun; Yue-Qin Wu,"ABSTRACT Observations recorded on ‘locations’ usually exhibit spatial dependence. In an effort to take into account both the spatial dependence and the possible underlying non-linear relationship, a partially linear single-index spatial regression model is proposed. This paper establishes the estimators of the unknowns. Moreover, it builds a generalized F-test to determine whether or not the data provide evidence on using linear settings in empirical studies. Their asymptotic properties are derived. Monte Carlo simulations indicate that the estimators and test statistic perform well. The analysis of Chinese house price data shows the existence of both spatial dependence and a non-linear relationship.",18.0, e14aaa386eebd7bbe7ac656ef52249731eabdff2,10.1177/0042098019897887,Urban house prices and investments in small and medium-sized industrial firms: Evidence from provinces of Iran,2020.0,,Hassan F. Gholipour,"Using data from provinces of Iran from 2005 to 2016 and applying panel cointegrating regressions, I find that increases in real house prices have a negative and statistically significant effect on investments in small and medium-sized industrial firms. There are at least three possible mechanisms through which increases in house prices adversely affect industrial entrepreneurship. They (1) generate housing construction investment opportunities with high returns, (2) attract many urban adults with tertiary education to the real estate brokerage industry, and (3) encourage people to save more to buy houses (which is one of the prerequisites for marriage in Iran).",18.0, cf16265d76cb2a334fa54d33f5e8cb817e67c45b,10.1108/ijhma-04-2017-0035,"Park proximity, crime and apartment prices",2018.0,,Asifa Iqbal; Mats Wilhelmsson,,18.0,http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217512 5560158772cca6a9ce3bc164aeb14df730c00078,10.1080/14445921.2009.11104273,Determinants of House Prices in New Zealand,2009.0,,Xin Janet Ge,,18.0, 9304f4227e15ad47d98e3d1b4a67e08f331927b6,10.14254/2071-789x.2016/9-2/5,"The Nature of Israel's Public Policy Aimed at Curbing the Rise in Property Prices from 2008-2015, as a Derivative of the Country's Governance Structure",2016.0,,Erez Cohen,"IntroductionThe considerable rise in Israel's property prices from 2008 to the present1 has put concerns of a developing real estate bubble on Israel's public agenda (Dovman et al., 2012). Volatile property prices are not unusual and occur in all countries with a free market, similar to the price of shares and commodities. Nonetheless, a historical perspective points to the notable contribution of real estate bubbles to the development of domestic economic and social crises. Two particularly noticeable cases of extreme volatility in domestic property prices are known to have led to acute long-term local and foreign economic crises. The first is the case of Japan in the early 1990s (the property bubble crisis), which generated much research (Weinstein, 1990; Ito & Iwaisako, 1995; Stone & Ziemba, 1993; Noguchi, 1994; Cargill, Hutchison & Ito, 1996; Noguchi, 1994; Schaede, 1996; Ohmi, 2010: Saxonhouse & Stem, 2002). The second is the US property crisis (the subprime crisis) in the first decade of the twenty first century, which has also been extensively studied (Kregel, 2008; Demyanyk & Van Hemert, 2011; Gjerstad & Smith, 2009; Blundell-Wignall & Atkinson, 2008; Gwinner & Sanders, 200",17.0,https://doi.org/10.14254/2071-789x.2016/9-2/5 bfbb7ad42d56c88d6f8e096cf19426072e44f05a,10.1068/a44627,"Featured Graphic. Spatial Inequality and Workplace Accessibility: The Case of a Major Hospital in Göteborg, Sweden",2012.0,,E. Erik; Ana Gil Solá; Anders Larsson,"This map visualizes the interrelation between car/public transport workplace accessibility differences and place of residence.(1) The case is based on a hospital with 4500 employees and is the city’s single largest workplace with a majority of female workers. The analysis shows that 53% of the low-income female workers at the hospital can save between 30 and 60 minutes daily if they commute by car instead of public transport, compared with 46% of the high-income men. In addition, only 33% of the low-income women can save up to 30 minutes per day by using car instead of public transport, compared with 42% of the high-income men. The pattern of accessibility difference in the map indicates that low-income groups in general may be overrepresented in the semiperiphery of the city, refl ecting a gentrifi cation process of the inner-city areas, which often mismatches with the location of low-incomedominated workplaces. Thus, affordability of housing seems to be an important determinant for the place of residence choice, since most areas with the least difference between car and public transport accessibility are located in the city centre where property prices are high. Furthermore, the ",17.0,http://journals.sagepub.com/doi/pdf/10.1068/a44627 31236e84175b169f162ce709cca3eb6c4f6b0a0c,10.1016/j.ssmph.2023.101595,"Geographically specific associations between county-level socioeconomic and household distress and mortality from drug poisoning, suicide, alcohol, and homicide among working-age adults in the United States",2024.0,SSM: Population Health,Xue Zhang; Shannon M. Monnat,"Background Mortality rates from drug poisoning, suicide, alcohol, and homicide vary significantly across the United States. This study explores localized relationships (i.e., geographically specific associations) between county-level economic and household distress and mortality rates from these causes among working-age adults (25–64). Methods Mortality data were from the National Vital Statistics System for 2014–2019. County-level socioeconomic distress (poverty, employment, income, education, disability, insurance) and household distress (single-parent, no vehicle, crowded housing, renter occupied) were from the 2009–2013 American Community Survey. We conducted Ordinary Least Squares (OLS) regression to estimate average associations and Geographically Weighted Regression (GWR) to estimate localized spatial associations between county-level distress and working-age mortality. Results In terms of national average associations, OLS results indicate that a one standard deviation increase in socioeconomic distress was associated with an average of 6.1 additional drug poisoning deaths, 3.0 suicides, 2.1 alcohol-induced deaths, and 2.0 homicides per 100,000 population. A one standard de",17.0,https://doi.org/10.1016/j.ssmph.2023.101595 609f19795df3057e1a30de284584a2321a57df4f,10.1371/journal.pone.0251960,Distancing the socially distanced: Racial/ethnic composition’s association with physical distancing in response to COVID-19 in the U.S.,2021.0,PLoS ONE,Joseph Gibbons,"Social distancing prescribed by policy makers in response to COVID-19 raises important questions as to how effectively people of color can distance. Due to inequalities from residential segregation, Hispanic and Black populations have challenges in meeting health expectations. However, segregated neighborhoods also support the formation of social bonds that relate to healthy behaviors. We evaluate the question of non-White distancing using social mobility data from Google on three sites: workplaces, grocery stores, and recreational locations. Employing hierarchical linear modeling and geographically weighted regression, we find the relation of race/ethnicity to COVID-19 distancing is varied across the United States. The HLM models show that compared to Black populations, Hispanic populations overall more effectively distance from recreation sites and grocery stores: each point increase in percent Hispanic was related to residents being 0.092 percent less likely (p< 0.05) to visit recreational sites and 0.127 percent less likely (p< 0.01) to visit grocery stores since the onset of COVID-19. However, the GWR models show there are places where the percent Black is locally related to r",17.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0251960&type=printable 8dc98427d4e7ceed94bdcf67951ede5ee1aaeea3,10.3390/ijgi9040217,Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach,2020.0,ISPRS Int. J. Geo Inf.,Yuxue Wang; Su Li; Xun Zhang; Dong Jiang; Meng-Meng Hao; Rui Zhou,"With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the",17.0,https://doi.org/10.3390/ijgi9040217 ab4f8fb40fdce0da7c26536b31d592bbae29af2b,10.1161/circheartfailure.121.009353,Neighborhood Socioeconomic Disadvantage and Hospitalized Heart Failure Outcomes in the American Heart Association Get With The Guidelines-Heart Failure Registry,2022.0,Circulation: Heart Failure,Vishal N. Rao; R. Mentz; A. Coniglio; Michelle D. Kelsey; M. Fudim; G. Fonarow; Roland A. Matsouaka; A. Devore; M. Caughey,"Background: Neighborhood socioeconomic status (SES) is associated with worse health outcomes, yet its relationship with in-hospital heart failure (HF) outcomes and quality metrics are underexplored. We examined the association between socioeconomic neighborhood disadvantage and in-hospital HF outcomes for patients from diverse neighborhoods in the Get With The Guidelines-Heart Failure registry. Methods: SES-disadvantage scores were derived from geocoded US census data using a validated algorithm, which incorporated household income, home value, rent, education, and employment. We examined the association between SES-disadvantage quintiles with all-cause in-hospital mortality, adjusting for demographics and comorbidities. Results: Of 593 053 patients hospitalized for HF between 2017 and 2020, 321 314 (54%) had residential ZIP Codes recorded. Patients from the most compared with least disadvantaged neighborhoods were younger (mean age 67 versus 76 years), more often Black (42% versus 9%) or Hispanic (14% versus 5%), and had higher comorbidity burden. Demographic-adjusted length of stay increased by ≈1.5 hours with each increment in worsening SES-disadvantage quintiles. Adjusted-morta",17.0,https://www.ahajournals.org/doi/pdf/10.1161/CIRCHEARTFAILURE.121.009353 b827a50a5b3fe735ba3475c0514ffecf35e91d7e,10.5198/jtlu.2021.1795,"If you build it, they will change: Evaluating the impact of commuter rail stations on real estate values and neighborhood composition in the Rotterdam–The Hague metropolitan area, the Netherlands",2021.0,Journal of Transport and Land Use,Amir Forouhar; D. van Lierop,"In many metropolitan regions, transit-oriented developments are built to motivate the use of sustainable travel by promoting urban growth within walking distances of public transport stations. Changes in residential property values are a common way to assess the success of transit-oriented developments. However, studies that focus on property values alone have reported mixed effects. This paper attempts to evaluate the land value impact around commuter rail stations by analyzing the change in property values within the context of the transformation of socio-spatial neighborhood attributes. The study sets out to estimate the effect of Randstad Rail stations using real estate transaction data of residential properties and neighborhood socio-spatial attributes in the Rotterdam–the Hague metropolitan area of the Netherlands covering a period from 1985 to 2018. Adopting a quasi-experimental design, the effect is estimated for properties within different catchment zones around three commuter rail stations using a Difference-in-Differences Model and Multivariate Analysis of Variance. The results demonstrate the overall negative effect of the Randstad Rail on the value of residential prope",17.0,https://www.jtlu.org/index.php/jtlu/article/download/1795/1585 3125ce7429979d234b67166d00ca61df1600ed12,10.3389/fpsyg.2021.782035,Can Housing Assets Affect the Chinese Residents’ Willingness to Pay for Green Housing?,2022.0,Frontiers in Psychology,Qian Wu; Ziyang Zheng; Wen-Bo Li,"As the development trend of the future housing field, green housing is an effective way to reduce pollution, save energy, and promote industrial upgrading. At the same time, the green house is of great significance to change the development mode of the construction industry and promote the sustainable development of the social economy. This study proposes a comprehensive research model to examine the influencing mechanism of residents’ intention to purchase green buildings. The proposed model is empirically tested using data collected from 1,338 urban residents in China. Based on logit, probit, and ivprobit models, factors such as personal characteristics, housing price, and the number of real estate ownership are selected to conduct empirical analysis and mechanism analysis on willingness that affects consumers’ purchase of green houses. The results show that housing assets significantly affect the willingness of householders to pay for green houses. The more houses they own, the higher their willingness to pay for a green house will be. Similarly, if the housing prices are higher, householders are more willing to buy a green house. The amount of housing assets will affect the wil",17.0,https://www.frontiersin.org/articles/10.3389/fpsyg.2021.782035/pdf dedc78eeba83acb9117d32031c13b48e68005615,10.1007/s13676-013-0030-4,Measuring the effects of transportation infrastructure location on real estate prices and rents: investigating the current impact of a planned metro line,2014.0,EURO Journal on Transportation and Logistics,Dimitrios E. Efthymiou; C. Antoniou,"The importance of modeling the interactions between transportation infrastructure and land use has been demonstrated through a large number of studies. The aim of this paper is, on the one hand, to measure the effect of transportation infrastructure and land-use characteristics on dwelling prices and rents in Thessaloniki, Greece, and, on the other, to investigate the current impact of a future metro line on the area. The results reveal a negative effect of the proximity to port and railway station on property prices and rents, and a positive impact of the proximity to the airport. The metro line, which is still under construction, has a negative impact on the purchase prices, while it does not significantly affect the rents. In the first case, this can be attributed to the negative externalities generated by the construction, which is generally not expected to be completed within the next 5 years, a fact that keeps the expected demand in low levels. This conclusion comes in accordance with other research works based on surveys in the area. In the second case, the variable is insignificant since rents are often short term which means the future implementation of the metro does not ",17.0,https://www.sciencedirect.com/science/article/pii/S2192437620301382/pdf 4b1717baf61a3bbd2fd3f53b93e410aa877ccf05,10.3390/urbansci7010018,Are Central Banks’ Monetary Policies the Future of Housing Affordability Solutions,2023.0,Urban Science,Chung Yim Edward Yiu,"Housing affordability is one of the major social problems in many countries, with some advocates urging governments to provide more accessible mortgages to facilitate more homeownership. However, in recent decades more and more evidence has shown that unaffordable housing is the consequence of monetary policy. Most of the previous empirical studies have been based on econometric analyses, which make it hard to eliminate potential endogeneity biases. This cross-country study exploited the two global interest rate shocks as quasi-experiments to test the impacts and causality of monetary policy (taking real interest rates as a proxy) on house prices. Global central banks’ synchronized reduction in interest rates after the outbreak of the COVID-19 pandemic in 2020 and then the global synchronized increase in interest rates after the global inflation crisis in 2022 provided both a treatment and a treatment reversal to test the monetary policy hypothesis. The stylized facts vividly reveal the negative association between interest rate changes and house price changes in many countries. This study further conducted a ten-country panel regression analysis to test the hypothesis. The results",17.0,https://www.mdpi.com/2413-8851/7/1/18/pdf?version=1675413767 af8b085301877ec2ccab7828e4b57013f0ac1f23,10.3390/su11020348,"Complex Spatial Morphology of Urban Housing Price Based on Digital Elevation Model: A Case Study of Wuhan City, China",2019.0,Sustainability,Zuo Zhang; Xin-Hai Lu; Min Zhou; Yan Song; Xiang Luo; Bing Kuang,"In a city, housing price varies with location. Thus, housing price plays an important role in detecting the spatial pattern of the city. Spatial interpolation methods have been widely used for simulating and predicting urban housing prices. In this paper, the Ordinary Kriging interpolation method is used for producing the digital elevation model (DEM) of urban housing prices. Based on the three-dimensional DEM of urban housing price, this paper develops a novel approach for geo–visual analytics of urban housing prices. To investigate and visualize the spatial morphology of housing price, we design the Water-flooding, Section-cutting and Belt-floating methods, and implement these methods with the 3D-analyst module in GIS environment. Then, we take Wuhan City as a case, apply this approach to analyze the complex spatial morphologic characteristics of the DEM for housing price and visualize the results from the multidimensional perspectives. The results show that the Water-flooding method effectively supports the investigation of the top areas of surface changes; Section-cutting method performs well in examining the profile or cross-section of the urban housing surface; and Belt-float",17.0,https://www.mdpi.com/2071-1050/11/2/348/pdf?version=1547200935 2518e2c110a78ff2c5142894a3904cd1bd1228db,10.1080/23748834.2022.2072058,Exploring inequities in housing affordability through an analysis of walkability and house prices by neighbourhood socioeconomic disadvantage,2022.0,Cities & Health,L. Gunn; T. Saghapour; B. Giles-Corti; G. Turrell,"ABSTRACT Cities are socio-spatially patterned, yet few researchers have explored the association between walkability and house prices by neighbourhood socioeconomic disadvantage, highlighting issues about housing affordability, equity, and liveability. We aimed to determine whether walkability and house prices differed by neighbourhood socioeconomic disadvantage. To test this, we used linear regression models of house prices stratified by quintiles of neighbourhood socioeconomic disadvantage at the suburb level in metropolitan Melbourne, Australia on walkability and its components (street connectivity, dwelling density, and destination access), and public transit access. Walkability was positively associated with house prices. In the stratified regressions, destination accessibility was associated with higher house prices whilst having poor access to transit was negatively associated with house prices. The association between walkability and house prices was weakest for the most disadvantaged areas, suggesting that houses in these areas were more affordable due, in part, to a lack of amenity. Future planning could redress the relationship between walkability and house prices by mak",17.0,https://www.tandfonline.com/doi/pdf/10.1080/23748834.2022.2072058?needAccess=true 403476ec5f39b46ace725f1094480d1f5f52005e,10.3390/buildings11020048,Macro-Impacts of Air Quality on Property Values in China—A Meta-Regression Analysis of the Literature,2021.0,,Jianing Wang; C. Lee; S. Shirowzhan,"Air pollution has received increasing attention in recent years, particularly in China, due to the rapid industrialisation that has wrought intense levels of air pollution. A number of studies, therefore, have been devoted to quantifying the impacts of air pollution on property value in China. However, the empirical results are somewhat mixed. This naturally raises questions of whether there is a significant relationship between air quality and housing prices and the plausible reasons for the mixed results in previous studies. This study aims to fill this gap by explaining the variations in the findings by a meta-regression analysis. To control for heterogeneity, a weighted least square model was used to explore the factors influencing the magnitude and significance of the air quality effect based on empirical estimates from 117 observations. This study confirms that air quality does have a discernible impact on housing prices beyond the publication bias. Besides, the types of air quality indicator and the air data source do significantly influence estimates through affecting both the magnitude of the elasticity and the partial correlation coefficient (PCC). Further, the selections",17.0,https://www.mdpi.com/2075-5309/11/2/48/pdf?version=1614143749 640c44dc4ccf145ce85e047fcef15dfc851fe2b9,10.1016/j.proeng.2016.11.625,Market Analysis of Housing Shortages in Malaysia,2016.0,,A. Olanrewaju; A. Aziz; Seong Yeow Tan; Lim-Tat Lee; N. Mine,"The annual supply of housing in Malaysia is approximately four units per 1000 of the population; this is less than the recommended 8-10 units per 1000 in developing countries, implying that the Malaysian housing deficit units are likely to be on the increase. While the market is the most efficient way to determine housing price, the capital market for [affordable] housing have a very weak mechanism that if left unregulated will be ineffective and inefficient. The cost of accommodation continues to increase despite various government measures in the form of taxes and subsidies. Through a case study approach and survey questionnaire, this study examines the operation of affordable housing market the factors that determine the cost of affordable housing. The case study involved one of the affordable housing schemes. The study demonstrates that to base affordable housing price on market prices are seriously deficient in that it ignore the basic essence of affordable housing provision. There is no real basis for economists’ strongly held believe that house prices must be determined by the market to be efficient. With the Kaiser-Meyer-Olkin measure 0.714, and Bartlett's test of sphericit",17.0,https://www.sciencedirect.com/science/article/pii/S1877705816339662/pdf 762436588c3e2b9e6042e432bed40cc28e30b225,10.1111/1467-8489.12449,Analysing the impact of COVID‐19 on urban transitions and urban‐regional dynamics in Australia,2021.0,Australian Journal of Agricultural and Resource Economics,C. Nygaard; S. Parkinson,"In this paper, we draw on insights from economic theory on urban growth, large shocks and spatial dynamics to assess COVID‐19 flow‐on effects and potential disruptive legacy in urban‐regional dynamics. Urban dynamics in Australia are assessed at national, regional and intra‐urban scales. Long‐term and short‐term urban dynamics are analysed against random growth, locational fundamentals and increasing returns theories of urban growth and adjustment. A focus in Australia and elsewhere is the potential effect of COVID‐19 on where people want to live, enabled in part by technological connectivity that releases some workers from proximity to work constraints when choosing a home. Our results suggest that urbanisation trends and adjustments to shocks differ for capital cities and noncapital cities. At the inter‐regional migration level, Australia’s largest urban system, Sydney, is characterised by a cointegration relationship between outmigration and Sydney property prices relative to other housing markets. At finer spatial scales, COVID‐19 had a negative impact on house prices within Sydney and may, for some micro‐geographies and/or towns and regional centres, lead to significant change",17.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1467-8489.12449 54b906f93a085057694a517e801adc64cef1c2e2,10.3390/su8030203,A Study on Estimation of Land Value Using Spatial Statistics: Focusing on Real Transaction Land Prices in Korea,2016.0,,Bongjoon Kim; Taeyoung Kim,"The aim of this research is to compare OLS (Ordinary Least Squares) and spatial regression models which are methods of calculating the traditional value of land—using data on the practical transaction price of land—and to enhance the applicability of estimation of official land assessment prices set by the Korean government while deducing policy implications for effective implementation. That is, as a way to overcome the limitations of the traditional regression model, we compare various Generalized Regression Models such as SLM (Spatial Lag Model), SEM (Spatial Error Model) with OLS. Consequently, an in-depth diagnosis is conducted to generate a proper estimation model for land pricing, and, also, the analysis focuses on vertical and horizontal equity using COD (Coefficient of Dispersion), COV (Coefficient of Variation) and PRD (Price-Related Differential). The results indicate that SEM is more appropriate than AIC (Akaike info criterion) and SC (Schwarz criterion) in terms of measuring log-likelihood, demonstrating that the spatial autocorrelation model is superior to the traditional regression model. It shows that the SEM is also the best among the tested models with regard to m",17.0,https://www.mdpi.com/2071-1050/8/3/203/pdf?version=1456396319 6551d5ef849d76334ca9d9d8e5de697ba2c4112c,10.24818/ejis.2022.24,Determinants of House Prices and Housing Affordability Dynamics in the Czech Republic,2022.0,European Journal of Interdisciplinary Studies,E. Hromada; K. Čermáková; Marian Piecha,"The property market in Central European Region countries share a number of common features among which privatization, restitution of property, massive regulation or underdeveloped financial market all of which contributed on persisting property market imbalances and continuous dynamic changes. These changes have recently been significantly exacerbated by the presence of the Covid-19 pandemic, the war in Ukraine and a significant increase in energy prices (heating of apartments and houses, production of building materials, etc.). It is currently difficult for investors and people looking for their own housing to predict the future development of housing prices and housing affordability. This article analyses the housing market trends in this region taking the example of the Czech Republic using unique primary statistical data. It offers a deeper insight into the trends present on this market, identifies significant determinants of housing prices and evaluates changes in housing affordability. Our research reveals why the property market trends may contribute to opening inequality scissors and thus economic stability. This research is based on primary statistical data mined by EVAL s",17.0,https://doi.org/10.24818/ejis.2022.24 aedad25070d346ed64416bf6b34b3038633a479d,10.1007/s12076-022-00302-y,Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation,2022.0,Letters in spatial and resource sciences,Massimiliano Crisci; F. Benassi; Hamidreza Rabiei‐Dastjerdi; G. Mcardle,"This paper offers an analysis of the supply of Airbnb accommodation in Rome, one of the main tourist destinations in the world, the third-largest city in Europe, by the number of Airbnb listings. The aim is to focus on the recent spatial trend of Airbnb listings, including the period of the COVID-19 pandemic, and highlight the main housing and socioeconomic characteristics of the neighbourhoods associated with a strong presence of Airbnb listings. The study is developed with quantitative methods and spatial regression (spatial lag and spatial error using OLS as a benchmark), based on data collected from the Inside Airbnb and Tomslee websites. In the period 2014–2019, the listing trend in Rome has been increasing in absolute numbers. After the start of the pandemic, the trend became negative, and the decline of Airbnb offerings is more substantial for shared accommodation. Airbnb supply is related to the distance from the city centre, the average income of the area, empty apartments, singles and the share of foreign residents coming from high-income countries. A signal of spatial diffusion of Airbnb listings emerges in the coastal area, even if they are increasingly concentrated in ",17.0,https://link.springer.com/content/pdf/10.1007/s12076-022-00302-y.pdf 4f34e7a4acece7912750440685549d3c08b22cd0,10.1016/j.jeconom.2024.105685,Panel data models with time-varying latent group structures,2023.0,Journal of Econometrics,Yiren Wang; P. Phillips; Liang-Jun Su,"This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism the model may have different numbers of groups and/or different group memberships before and after the break. With the preliminary nuclear-norm-regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is shown that the break point, the number of groups and the group memberships can each be estimated correctly with probability approaching one. Asymptotic distributions of the estimators of the slope coefficients are established. Monte Carlo simulations demonstrate excellent finite sample performance for the proposed estimation algorithm. An empirical application to real house price data across 377 Metropolitan Statistical Areas in the US from 1975 to 2014 suggests the presence both of structural breaks and of ch",17.0,https://doi.org/10.1016/j.jeconom.2024.105685 ebb9ed71f15fa99b443bbf874eb355e0d70207b1,10.4236/ijis.2013.34017,A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study,2013.0,,I. S. H. Bahia,"This paper aims to demonstrate the importance and possible value of housing predictive power which provides independent real estate market forecasts on home prices by using data mining tasks. A (FFBP) network model and (CFBP) network model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets. This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing targets” is a 1 × 506 matrix of median values of owner-occupied homes in $1000’s. The result in this paper concludes that which one of the two networks appears to be a better indicator of the output data to target data network structure than maximizing predict. The CFBP network which is the best result from the Output_network for all samples are found from the equation output = 0.95 * Target + 1.2. The regression value is approximately 1, (R = 0.964). That means the Output_network is matching to the target data set (Median value of owner-occupied homes in $1000’s), and the percent correctly predict in the simulation sample is 96%.",17.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=37583 5aeea4922b03eec1ebf5b68b1b57fcbc6cfc2b72,10.1177/00420980221121088,Age segregation and housing unaffordability: Generational divides in housing opportunities and spatial polarisation in England and Wales,2022.0,Urban studies,A. Sabater; N. Finney,"Age is an important known driver of residential sorting, yet little is understood about how age segregation is affected by housing unaffordability. This relationship is particularly pertinent given trends of increasing housing inequalities and population ageing, in Europe and elsewhere. Using harmonised population data for small areas linked with local house price statistics and household incomes in England and Wales, this paper examines the scale of, and links between, residential age segregation and housing unaffordability. The results reveal a strong association between increasing housing unaffordability (for sales and rentals) and increasing residential age segregation (beyond other local characteristics). This association is particularly marked in urban and rich (least deprived) areas. This points to increasing spatial polarisation along the intersections of wealth and age: not only are the wealthiest parts of the country, where housing is particularly unaffordable, becoming increasingly demarcated socio-economically but also by age. This implies that age-related life course processes are integral to the trends observed more broadly of increasing socio-spatial polarisation.",17.0,https://doi.org/10.1177/00420980221121088 395c5d2a8a1503c507d1e5afde3efe9eed74bfcd,10.1016/j.cities.2021.103238,Pricing the value of the chance to gain admission to an elite senior high school in Beijing: The effect of the LDHSE policy on resale housing prices,2021.0,,Jun-su Han; Liyuan Cui; Hua-Yi Yu,"Abstract Elite schools are highly sought after worldwide. Measuring the influence of elite schools on housing prices is an important issue not only to researchers but also to all residents. On March 19, 2015, the Beijing municipal government enacted a policy called locking districts for senior-high-school admission examination (hitherto referred to as LDHSE). This policy prevents senior high schools in the Xicheng, Dongcheng, and Haidian districts, where the most elite senior high schools in Beijing are located, from admitting students from other districts. This study explores the effect of the LDHSE policy on housing prices, and estimates the value of the chance to gain admission to an elite senior high school in Beijing, China. Using the difference-in-difference (DID) + regression discontinuity design (RD) methodology, we find an overall 5.4% rise in housing prices in locked districts. After narrowing the bandwidths, we find a rise of approximately 6.4%, 3.4%, 2.8%, 3.5%, and 2.7% for bandwidths within 5 km, 2 km, 1 km, IK optimal bandwidth 2.687 km, and CCT optimal bandwidth 1.680 km, respectively. Furthermore, the LDHSE increases the transaction volume, supporting the argument ",17.0, 4013761638fb0d7734ddccf910563b4e4577a951,10.3390/ijgi8110471,Impact of a Vicinity of Airport on the Prices of Single-Family Houses with the Use of Geospatial Analysis,2019.0,ISPRS Int. J. Geo Inf.,R. Cellmer; M. Bełej; Jan Konowalczuk,"This article analyses the adverse impact of Chopin Airport in Warsaw on the prices of single-family houses located within the aircraft noise impact zone. The specific feature of the largest airport in Poland is its location within the city limits and the resulting direct surroundings of both multi- and single-family housing developments. Not only is the nuisance due to the proximity of the airport resulting from the actual exposure to an excessive noise level but also from legal restrictions associated with the Limited Use Area (LUA). The study used statistical modeling by applying a classic multiple regression model, spatial autoregressive model and geographically weighted regression model. Moreover, Geographical Information System (GIS) tools and geostatic modeling were used to visualise the results. The modeling results clearly show the significant impact of the neighborhood nuisance and the related spatial distribution of real estate prices. In addition, the geographically weighted regression model indicates that the proximity to an airport adversely affects the rate of price changes over time.",17.0,https://www.mdpi.com/2220-9964/8/11/471/pdf?version=1571998001 d4cee2e00925075ff80d2a12883fe1ecfcd543fc,10.1016/j.procs.2012.04.142,Cluster analysis on city real estate market of China: based on a new integrated method for time series clustering,2012.0,International Conference on Conceptual Structures,Kun Guo; Jue Wang; Gushan Shi; Xue-Hui Cao,"Abstract After the reform of urban housing system in 1998, China real estate market had a rapid growth in recent years, while house price was increasing sharply. Using the House Price Indices of 70 cities in China from CREIS (China Real Estate Index System), we found that the house price of each city had an upward tendency with some certain stages. However, different cities also had their distinctive features. In this paper, a new integrated method for time series clustering is employed to do cluster analysis on city real estate market of China. The time series are firstly divided into several stages mainly based on the changes in government policy using wavelet analysis with expert experience. Then the variables that describe the character of each stage such as average growth rate and volatility are used as attributes of each city. Consequently, DBScan algorism for normal clustering can be used and the results show that there are several categories of growth modes of city real estate markets while the macro-control policies had different effect on each category.",17.0,https://doi.org/10.1016/j.procs.2012.04.142 59f4ff5c7578141e64d6717c32a7e1579cd3e514,10.3390/economies10030064,Predicting House Prices Using DMA Method: Evidence from Turkey,2022.0,Economies,N. Hacıevliyagil; Krzysztof Drachal; I. Eksi,"The aim of this study is to analyze the dynamics of the housing market in Turkey’s economy and to examine the impact of variables related to housing prices. Preferred by many international housing investors, Turkey hosts profitable real estate investments as one of the developing countries with a shining housing market. This study applies the dynamic model averaging (DMA) methodology to predict monthly house price growth. With the increasing use of information technologies, Google online searches are incorporated into the study. For this purpose, twelve independent variables, with the Residential Property Price Index as the dependent variable, were used in the period January 2010–December 2019. According to the analysis results, it was observed that some variables, such as bond yields, the level of mortgages, foreign direct investments, unemployment, industrial production, exchange rates, and Google Trends index, are determinants of the Residential Property Price Index.",17.0,https://www.mdpi.com/2227-7099/10/3/64/pdf?version=1646913119 624663dff1b3bf35814a9f24967c7be02207f074,10.2139/ssrn.4688022,What Works and for Whom? Effectiveness and Efficiency of School Capital Investments Across the U.S,2024.0,Social Science Research Network,B. Biasi; Julien Lafortune; David Schönholzer," This paper identifies which investments in school facilities help students and which and are valued by homeowners. Using novel data on school district bonds, test scores, and house prices across 29 U.S. states and a research design based on narrowly decided elections with staggered timing, we find that increased capital spending in schools significantly improves test scores and is efficient on average. However, the effects vary widely depending on the type of project and the characteristics of the school district. Investments in essential infrastructure, such as HVAC systems or pollutant removal, yield notable improvements in student performance, while expenditures on athletic facilities show no measurable academic benefit. Socio-economically disadvantaged districts gain disproportionately from capital investments, even after accounting for project type, yet these districts typically underinvest in such projects.",17.0,https://doi.org/10.2139/ssrn.4688022 ab191331e36cbad09e63b15fe6718ff9ef7bb99d,10.1016/j.cities.2021.103237,Does school choice program affect local housing prices?: Inter- vs. intra-district choice program,2021.0,,Hyunjun Park; Alan Tidwell; Sungho Yun; Chang-Ha Jin,"Abstract We examine the effect of inter-district and intra-district high school programs on the residential real estate market in Seoul, South Korea, focusing on the switching effect from intra-district zoning to inter-district school choice. Data consist of 85,744 transaction-based housing observations corresponding to the 2008–2012 period surrounding South Korea's education policy change that directly affects 24 high schools. We employ a difference-in-differences (DID) methodology overcoming potential mis-specification bias while controlling for school, locational, and housing characteristics; and, find that switching from intra-district to an inter-district high school choice program decreases housing prices by an average of 4.7% within 0.5 km after the policy change was announced and another 5.3% after school admission. The observed price discount is ameliorated as the location boundary surrounding the affected schools is extended. This result suggests that the policy change reduced the economic barrier of higher housing prices near top performing schools and increased socio-economic diversity and geographic educational parity across the socio-economic spectrum. However, the cr",17.0, 5e2b1180b83c8a4300a6329dde6caf45b6769698,10.1007/978-3-030-23786-8_4,Spatial Implications of EPC Rankings Over Residential Prices,2019.0,Values and Functions for Future Cities,Carlos Marmolejo-Duarte; Ai Chen; M. Bravi,"Empirical evidence has found a correlation between Energy Performance (EPC) rankings and housing prices. Though, little attention has been devoted to research the spatial implications of EPC marginal prices. This paper attempts to fill this gap using a geographical-weighted-regression, departing from listing prices for Barcelona. The results suggest the existence of a premium equivalent to 1.7% for each EPC ranking, which nearly double the previous reported evidence in this city. Also, the impact shows a non-stationary distribution across urban space. As a matter of fact, the higher the impact, the lower the price of apartments and their quality. The apartments portraying the larger impact are located in peripheral areas inhabited by medium and low-income population. So, the equity of less wealthy population witnesses the largest brown discount.",17.0,http://hdl.handle.net/2117/175313 fbca687495ec1c96bddbdc0ba4ca22d0417a1385,10.1080/09599916.2018.1513057,House prices and proximity to kindergarten – costs of distance and external effects?,2018.0,Journal of Property Research,Theis Theisen; A. Emblem,"ABSTRACT Parents accompany children to day-care, implying costs of time and money. Distance to kindergarten may therefore be an important locational attribute, which is likely to be discounted into house prices. We account for this through a theoretical model of house price formation, incorporating not only monetary and time costs associated with accompanying children to a kindergarten, but also possibly negative external effects of kindergartens on their immediate vicinity. Our theoretical model predicts that house prices increase as distance to kindergarten decreases, reach a peak, and then decline as one come very close to a kindergarten. We use a large sample of house transactions from a Norwegian town to explore the relationship between house prices and the distance to kindergarten. The empirical results support the prediction that house prices decline as distance to kindergarten increases, but we find no significant drop in house prices in the immediate vicinity of kindergartens. The results may be of interest to several actors in real-estate markets, perhaps particularly to urban planners and real-estate developers when considering the location of kindergartens.",17.0, add78fb695d60a689b2585c010a4a0dd241301f2,10.1016/j.ijdrr.2020.101543,Homeowners’ choice when the government proposes a managed retreat,2020.0,,Cuong Nguyen,"Abstract The Canterbury earthquake sequence (2010–2011) badly damaged land and created new risks from rockfalls and cliff collapses in several residential areas in Christchurch, New Zealand. This affected about 8000 houses which were then reclassified as ‘Red Zoned’ and owners were offered a buy-out by the New Zealand government. The government provided two options to the affected homeowners, based on insurance compensation for damage incurred, or on compensation from the government. This paper examines how the determinants of owners' choices, including property value, level of earthquake damage and household's demographic information, affect homeowner's choice between these options. This also investigates how peer effects influenced owners and led to sub-optimal decision-making.",17.0, bef12f85b1060a1107e9bd3fdf01ea3be0a03dee,10.2139/ssrn.3478900,Highly Disaggregated Land Unavailability,2019.0,Social Science Research Network,Chandler Lutz; Benjamin M. Sand,"We combine high-resolution satellite imagery with modern machine learning techniques to construct novel datasets that capture the geographic determinants of U.S. housing supply. This Land Unavailability (LU) measure is a markedly more accurate house price predictor than the popular proxy of Saiz (2010). LU is also uncorrelated with housing demand proxies, supporting its use as an instrument for house prices. We apply LU to fundamental housing finance problems to provide substantially more precise housing wealth elasticity estimates; novel empirical tests of the supply-side speculation theory; and new evidence on the relationship between house prices and self-employment during COVID-19. JEL Classification: R30, R31, R20 ;",17.0, a37a92f2696a34f55c7af155a580e60004fa434d,10.1007/s11146-008-9134-2,The Housing Price Impact of Covenant Restrictions and Other Subdivision Characteristics,2010.0,,W. Rogers,,17.0,https://autopapers.ssrn.com/sol3/papers.cfm?abstract_id=1504182 443a582a1db48a2aad10dfd2ff51939e5b5bb3bb,10.1080/02673030701608183,Landscaping Attributes and Property Buyers' Profiles: Their Joint Effect on House Prices,2007.0,,F. D. Rosiers; M. Thériault; Y. Kestens; P. Villeneuve,,17.0, fab80a014e7c69903f33920d8b77abacdc5a7fbb,10.1111/1540-6229.t01-1-00201,"FHA Mortgage Discount Points, House Prices and Consumer Behavior",1979.0,,K. L. Guntermann,,17.0, 29983f9ade705f981b012495b6339aac7ac62359,10.3368/le.84.2.188,Finding the Missing Premium: An Explanation of Home Values within Residential Community Associations,2008.0,Land Economics,J. Groves,,17.0, a907287eba86306b6de0fa7078b9b503d1c5a9b3,10.2991/mcei-18.2018.54,Forecasting Second-hand Housing Price using Artificial Intelligence and Machine Learning Techniques,2018.0,,Tao Fu,"In this era of information explosion, the development of science and technology changes with each passing day. Therefore, the automatic analysis of scientific and technological trends aims to help scientists extract useful information from a large number of academic conferences and scientific and technological documents, which is of great practical significance. Artificial Intelligence give us an useful techniques, this paper we use using artificial intelligence and machine learning techniques to fit and forecast house price, From the point of view of model, the prediction effect of support vector machine is the best, with strong stability, while neural network is the worst. As a contrast, linear regression and random forest have little difference between them. Introduction Artificial Intelligence is a new science of technology to study, develop and extend human intelligence theory, method, technology and application system. Artificial intelligence is the study of computer to simulate certain thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). Enable the computer to achieve a higher level of application. Artificial intelligence will",16.0,https://download.atlantis-press.com/article/25898502.pdf 3a8f5a2d0ac3a4b301dce77474bb4912d009e059,10.4000/cybergeo.6225,"The impact of gated Communities on property values: evidence of changes in real estate markets -Los Angeles, 1980-2000",2005.0,,R. Le Goix,"The paper focuses on how gated communities, as private means of providing public infrastructure and security, real estate products and club-economies, produce changes in housing market patterns. Based on an empirical study of Los Angeles (California) data, it aims to trace to what extent gates and walls favor property values and if the presence of gated communities produces over time (1980-2000) a deterrent effect on non-gated properties abutting the enclave, or close to it.Resulting from a demand for security, gated communities are a leading offer from the homebuilding industry. But their spread emerges from a partnership between local governments and land developers. Both agree to charge the homebuyer with the cost of urban sprawl (construction and maintenance costs of infrastructure within the gates). Such a structuring of residential space is particularly desirable on the urban edges, where the cost of urban sprawl exceeds the financial assets of local public authorities. New private developments provide local governments with new wealthy taxpayers at almost no cost. As compensation, the homebuyer is granted private and exclusive access to sites and amenities (lakes, beaches, e",16.0,https://doi.org/10.4000/cybergeo.6225 ca3fca702c30a4041ec69213d9e189ab5a8e3e9c,10.3390/ijgi14030131,"Unraveling Spatial Nonstationary and Nonlinear Dynamics in Life Satisfaction: Integrating Geospatial Analysis of Community Built Environment and Resident Perception via MGWR, GBDT, and XGBoost",2025.0,ISPRS Int. J. Geo Inf.,Di Yang; Qiu-Jie Lin; Haoran Li; Jin-Liu Chen; Hong Ni; Peng-Cheng Li; Ying Hu; Hao-Qi Wang,"Rapid urbanization has accelerated the transformation of community dynamics, highlighting the critical need to understand the interplay between subjective perceptions and objective built environments in shaping life satisfaction for sustainable urban development. Existing studies predominantly focus on linear relationships between isolated factors, neglecting spatial heterogeneity and nonlinear dynamics, which limits the ability to address localized urban challenges. This study addresses these gaps by utilizing multi-scale geographically weighted regression (MGWR) to assess the spatial nonstationarity of subject perceptions and built environment factors while employing gradient-boosting decision trees (GBDT) to capture their nonlinear relationships and incorporating eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy. Using geospatial data (POIs, social media data) and survey responses in Suzhou, China, the findings reveal that (1) proximity to business facilities (β = 0.41) and educational resources (β = 0.32) strongly correlate with satisfaction, while landscape quality shows contradictory effects between central (β = 0.12) and peripheral zones (β = −0.09). (2) XGB",16.0,https://doi.org/10.3390/ijgi14030131 74a46d535c736e19bb0546c84087f4efd6699671,10.3390/su8101053,Chinese Housing Reform and Social Sustainability: Evidence from Post-Reform Home Ownership,2016.0,,Li-Shan Xiao; Quanyi Qiu; Lijie Gao,"Since 1978, China has undergone an institutional reform, from a welfare-oriented housing allocation system to a market-oriented one. But with high housing prices, affordability is a major obstacle to home ownership for Chinese citizens. Now, the government has started to change the goal of housing policy from present economic benefits to sustainable housing, so future generations will have a decent place to live. Housing is an important indicator for social stratification, and home ownership, which is an important component of social sustainability in the Chinese context, is influenced by multiple factors that vary across countries. Although China has a long tradition of home ownership, there is a lack of comprehensive research on post-reform housing inequality. By undertaking a large-scale field study in the city of Xiamen, our research explored to what extent home ownership varies across socio-economic classes, and improves understanding of the reasons behind home ownership inequality. It was discovered that people have a variety of resources from which housing can be obtained, and that commercial housing served as the primary housing source, although, due to path dependence, pub",16.0,https://www.mdpi.com/2071-1050/8/10/1053/pdf?version=1476951437 a47de418f520e6a57bdc090332753fdbeb2f8876,10.1371/journal.pone.0209065,Accounting for peoples’ preferences in establishing new cities: A spatial model of population migration in Kuwait,2018.0,PLoS ONE,N. Alghais; D. Pullar; E. Charles-Edwards,"Modelling of internal migration to new cities is challenging, yet necessary to ensure that these newly established urban areas will be populated and function as intended. In the State of Kuwait, there is a unique set of push and pull factors: government subsidised housing for citizens, the existence of a single urban area, and the initiation of a new and ambitious master plan for the construction of 12 new cities, which are expected to attract not only locals, but also international residents and businesses. On top of these factors, there is an unusual demographic situation, as non-citizens outnumber Kuwaiti citizens by a factor of 2.3, with these groups having widely different preferences in terms of housing. Currently, there is no plan to take these resident groups’ opinions into consideration for the new cities project. Besides, the current study simulates the impacts of the involvement of residents in urban planning. Samples from resident groups (citizens and non-citizens) participated in targeted surveys and useful answers were extracted in relation to the migration likelihood, push and pull factors that may affect their decisions, spatial preferences for new cities and their ",16.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0209065&type=printable c2715d347769411c1056dba7d4f40c1140f804b2,10.3390/ijgi12090371,Nonlinear Hierarchical Effects of Housing Prices and Built Environment Based on Multiscale Life Circle - A Case Study of Chengdu,2023.0,ISPRS Int. J. Geo Inf.,Yandi Song; Shaoyao Zhang; Wei Deng,"Determining the optimal planning scale for urban life circles and analyzing the associated built environment factors are crucial for comprehending and regulating residential differentiation. This study aims to bridge the current research void concerning the nonlinear hierarchical relationships between the built environment and residential differentiation under the multiscale effect. Specifically, six indicators were derived from urban crowdsourcing data: diversity of built environment function (DBEF1), density of built environment function (DBEF2), blue–green environment (BGE), traffic accessibility (TA), population vitality (PV), and shopping vitality (SV). Then, a gradient boosting decision tree (GBDT) was applied to derive the analysis of these indicators. Finally, the interpretability of machine learning was leveraged to quantify the relative importance and nonlinear relationships between built environment indicators and housing prices. The results indicate a hierarchical structure and inflection point effect of the built environment on residential premiums. Notably, the impact trend of the built environment on housing prices within a 15 min life circle remains stable. The effe",16.0,https://www.mdpi.com/2220-9964/12/9/371/pdf?version=1694003247 f3404c58fbc3a45be8a10a81c3f6661546c9c592,10.3390/land11071095,"Spatial Heterogeneity of Public Service Facilities in the Living Circle and Its Influence on Housing Prices: A Case Study of Central Urban Dalian, China",2022.0,Land,Jin-Liang Hao; Hai-Long Ma,"The spatial layout of public service facilities (PSFs) markedly influences residents’ quality of life. Based on Baidu map data, spatial information on 27,552 PSFs across eight categories was collected for urban Dalian, China, and analyzed using the nearest neighbor index and nuclear density. Then, PSF accessibility across eight dimensions of residential quarters was calculated based on the cumulative opportunity method, and its impact on housing prices was analyzed. The results revealed the following: (1) The degree of spatial agglomeration for PSFs varied, with that of business facilities being higher than that of other public welfare facilities. The distribution of business facilities was characterized by a dense center and sparse periphery, whereas public welfare facilities were laid out in a relatively balanced “multi-center” distribution across the study area. (2) Significant spatial differences in the number and types of accessible resident facilities were identified. The number of accessible PSFs in the core area of central urban regions was large and the types were relatively complete, whereas the accessible PSFs in the western and northern marginal areas were limited in nu",16.0,https://www.mdpi.com/2073-445X/11/7/1095/pdf?version=1658140092 2b992709053feda78963b3c3e82b0631d100769e,10.1108/ajeb-09-2024-0103,Bank non-performing loans research around the world,2025.0,Asian Journal of Economics and Banking,P. Ozili,"PurposeThis article presents a literature review of bank non-performing loans (NPLs) research around the world and suggests directions for future research.Design/methodology/approachThe study used the thematic and bibliometric literature review methodologies to present a review of the recent NPL literature that have emerged since 2020.FindingsSignificant NPL research has emerged from the European, Asian and African regions, while fewer research studies have emerged from the Asia–Pacific, North America, Latin America and Caribbean regions as well as from the South Asian Association for Regional Cooperation and Organization for Economic Cooperation and Development countries. The new NPL determinants in the recent literature are corporate governance, fintech, financial inclusion, country risks, regulatory quality, political risks, shadow banking activity, the COVID-19 pandemic, public and/or external debt, country risks, real house prices and the independence of the central bank. The common regional NPL determinants are corruption, gross domestic product (GDP), debt, loan growth, inflation, capital adequacy ratio, lending rate, competition, the regulatory environment and GDP growth. T",16.0,https://www.emerald.com/insight/content/doi/10.1108/AJEB-09-2024-0103/full/pdf?title=bank-non-performing-loans-research-around-the-world b8cb2c0c68655087bee4ebb25c0480adc4a67c6a,10.3390/buildings14113517,Examining the Impact of the Built Environment on Multidimensional Urban Vitality: Using Milk Tea Shops and Coffee Shops as New Indicators of Urban Vitality,2024.0,Buildings,Ziqi Xu; Jiang Chang; Fangyu Cheng; Xiaoyi Liu; Tianning Yao; Kuntao Hu; Jingyu Sun,"Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subw",16.0,https://doi.org/10.3390/buildings14113517 c4b70475caa7925d9b58d7180ef1d87065a8d259,10.3390/sym12081329,"Research on Accurate House Price Analysis by Using GIS Technology and Transport Accessibility: A Case Study of Xi'an, China",2020.0,Symmetry,Chaochen Xue; Yongfeng Ju; Shuguang Li; Qilong Zhou; Qingqing Liu,"Based on the symmetrical public transportation network data of Xi’an, China obtained by geographic information system (GIS) technology in 2019, three urban public transportation indexes of walking accessibility, bus accessibility, and metro accessibility were established, and a real estate price prediction model was built by using several machine learning algorithms to predict and analysis the housing price in Xi’an, China. Firstly, the symmetrical road network data and real estate property data of Xi’an were collected and preprocessed, secondly, the spatial syntax theory and distance calculation method were applied to establish three indexes of traffic accessibility; finally, taking the house property data and the calculated traffic accessibility indexes as the characteristic index, the real estate price prediction model of Xi’an was constructed by using the random forest algorithm (RF), lightweight gradient lift algorithm (LGBM), and gradient lifting regression tree algorithm (GBDT). The prediction accuracy of the final model is 89.2% and the root-mean-square error is 1761.84. The results show that the accessibility of bus and metro to some extent represent the convenience of pub",16.0,https://www.mdpi.com/2073-8994/12/8/1329/pdf?version=1597035960 8cb8c23f227373ecd7ed10930a6dedbe1cf9462c,10.3390/land14050957,"Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las Vegas",2025.0,Land,Jingyi Liu; Yuxuan Cai; Xi-Wei Shen,"Over the past two decades, research on residential segregation and environmental justice has evolved from spatial assimilation models to include class theory and social stratification. This study leverages recent advances in machine learning to examine how environmental, economic, and demographic factors contribute to ethnic segregation, using Las Vegas as a case study with broader urban relevance. By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. To improve model transparency, the SHAP (SHapley Additive exPlanations) method was employed, identifying key variables influencing segregation outcomes. This interpretability framework helps clarify variable importance and interaction effects. The findings reveal that housing prices and poor environmental quality disproportionately affect minority populati",16.0,https://www.mdpi.com/2073-445X/14/5/957/pdf?version=1745918836 64ebea43623740b29d4a4657269a61406cbe308a,10.1186/1471-2458-6-5,Council tax valuation band predicts breast feeding and socio-economic status in the ALSPAC study population,2006.0,BMC Public Health,N. Beale; Gillian Kane; Mark D. Gwynne; C. Peart; Gordon J. Taylor; D. Herrick; A. Boyd; Alspac Study Team,"BackgroundBreast-feeding rates in the UK are known to vary by maternal socio-economic status but the latter function is imperfectly defined. We test if CTVB (Council Tax Valuation Band – a categorical assessment of UK property values and amenities governing local tax levies) of maternal address predicts, in a large UK regional sample of births, (a) breast-feeding (b) personal and socio-economic attributes of the mothers.MethodsRetrospective study of a subset (n.1390 selected at random) of the ALSPAC sample (Avon Longitudinal Study of Parents and Children), a large, geographically defined cohort of mothers followed from early pregnancy to 8 weeks post-delivery. Outcome measures are attitudes to breast-feeding prior to delivery, breast-feeding intention and uptake, demographic and socio-economic attributes of the mothers, CTVB of maternal home address at the time of each birth. Logistic regression analysis, categorical tests.ResultsStudy sample: 1360 women divided across the CTVBs – at least 155 in any band or band aggregation. CTVB predicted only one belief or attitude – that bottle-feeding was more convenient for the mother. However only 31% of 'CTVB A infants' are fully breast fed",16.0,https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/1471-2458-6-5 7afb4b58d5eb12b4b89f862803f0755a960f632f,10.5539/jsd.v5n11p65,"Implication of Intangible Location Attributes on Residential Segregation in Jos, Nigeria",2012.0,,A. Aliyu; R. Kasim; D. Martin; M. Diah; H. Ali,"Residential segregation could be regarded as a process whereby two or more distinct communities who formerly lived together separate from one another due to many factors. Residential segregation is not only applicable to small communities but rather to a larger region. As a result of the ongoing civil unrest that engulfed the city of Jos, there has been a process of residential mobility and relocation among people of different faith. The paper is aimed at examining the implication of intangible location attributes on residential mobility, segregation and relocation in Jos town. Stratified random sampling technique was employed in order to come up with the sample needed to conduct the research. The data needed for the research were retrieved through structured, semi-structured and unstructured interview method of data collection. A qualitative method and approach of data analysis through the use of a thematic network analysis was incorporated in order to analyse the data gotten from the interview survey. The results uncovered that residential segregation in the study area leads to change in the residential pattern of Jos town. The variations and trends in the sales and rental value ",16.0,https://ccsenet.org/journal/index.php/jsd/article/download/21750/14163 9ee0a2d1b4d602b61563df94a293352006227e5f,10.1332/175795920x15846933259695,Young British adults’ homeownership circumstances and the role of intergenerational transfers,2020.0,Longitudinal and life course studies,Ellie Suh,"Despite the continuing preference for homeownership, it has become increasingly difficult for young adults to own a home in Britain. House prices have increased faster than real earnings between the mid-1990s and the 2010s, resulting in significantly deteriorated affordability. Mortgage products have also become less accessible, as a large deposit has been required to secure the loan after the financial crisis of 2008/09. Previous studies point to the increasing role of intergenerational transfers in filling this gap. Some young adults obtain help from family to become homeowners, either receiving monetary support or by saving through living at the parental home. Using the Wealth and Assets Survey, this study attempts to examine the effect of these two types of family financial support on young adults’ homeownership circumstances, and controlling for other characteristics such as parental homeownership. First, it examines the characteristics of homeowners among young adults cross-sectionally using logistic regression. Second, by focusing on the non-homeowner subsample it analyses the effect of direct (money) and indirect (co-residence) family support on young adults’ entry to h",16.0,https://bristoluniversitypressdigital.com/downloadpdf/journals/llcs/11/3/article-p383.pdf 8bf0b2344e4a2a4da64cc5c38a6c487ad9bb7953,10.17016/feds.2009.18,"Fiscal amenities, school finance reform and the supply side of the Tiebout market",2009.0,,Byron Lutz,"This study asks if local governments which provide a high level of public services per tax dollar attract housing capital. The first portion of the paper examines large shifts in property tax burdens induced by an unusual school finance reform in the state of New Hampshire. The estimates suggest that, in most of the state, communities with a reduced tax burden experience a large increase in residential construction. In the area of the state near the region's primary urban center (Boston), however, the shock clears through a price adjustment--i.e. by capitalizing into property values. The differing responses are attributed to differing housing supply elasticities. Furthermore, the shock induced communities with a lowered tax burden to enact more stringent land use regulations. The second portion of the paper uses a national sample and exploits variation in education spending levels arising from 1980s era school finance reforms. The results confirm the findings from New Hampshire--fiscal amenities have a significant impact on the location of residential capital and the impact is largest outside of dense, urban areas. These results, which are interpreted through the lens of a simple t",16.0,https://doi.org/10.17016/feds.2009.18 797dd1448049e211c1e3df76aca51a277ad16075,10.20955/r.2016.159-188,Sales of Distressed Residential Property: What Have We Learned from Recent Research?,2016.0,,Jeffrey P. Cohen; Cletus C. Coughlin; Vincent Yao,"During the housing bust many homeowners found themselves “underwater”—the amount they owed on their mortgages exceeded the value of the associated property—and they either could not or possibly chose not to stay current on their mortgage payments. As a consequence, sales of so-called distressed properties, often after a foreclosure, became commonplace. This spurred numerous research papers on various related issues. The authors’ review summarizes the research findings on three topics: the impact of changes in housing prices on foreclosures; the impact of foreclosure on the sales price of the foreclosed house; and the impact of foreclosure on the sales prices of nearby houses. Not surprisingly, declining housing prices are associated with increasing foreclosure rates; however, various other factors, such as a job loss or expected housing prices, can also play an important role. This review highlights various theoretical and econometric issues that have raised doubts about the accuracy of estimated price impacts of foreclosures and led to numerous refinements of the subsequent empirical analysis. Estimates of the own foreclosure discount generally range from near zero to 28 percent, ",16.0,https://files.stlouisfed.org/research/publications/review/2016-09-12//sales-of-distressed-residential-property-what-have-we-learned-from-recent-research.pdf 62321b935e4b4223228ccacf41a3e2385ca183cd,10.1016/j.jtrangeo.2020.102790,"Should I stay or should I go? A survey analysis of neighborhood change and residential mobility concerns around new light rail stations in Charlotte, NC",2020.0,,I. Nilsson; J. Schuch; E. Delmelle; K. Canales,"Abstract In this article, we examine the effects of rail transit investments on residents' stated mobility intentions and perceptions of neighborhood changes using a survey analysis in Charlotte, North Carolina. We ask residents in neighborhoods along a new light rail line about their reasons for residing in their current neighborhood, thoughts about moving and the light rail's effect on their neighborhood. To control for city-wide housing market pressures, responses from one station-adjacent neighborhood are compared to responses from residents in a similar neighborhood elsewhere in the city while controlling for individual characteristics. Using a mixed-methods research approach, we find that while residents attribute some changes in their property values and rents to the light rail, it is only one of many factors affecting their neighborhood. Light rail also does not appear to affect residents' stated propensity to move out of these neighborhoods. Survey respondents' view of the light rail's effect on their neighborhood is also positive, on average. We find that the stated likelihood of moving is not related to the distance to the station nor to how frequently a resident uses th",16.0,https://doi.org/10.1016/j.jtrangeo.2020.102790 7443f4f7cf14bd7c561186705ec36a5dc0f4ee26,10.1515/remav-2020-0013,The Impact of Road Traffic Noise on Housing Prices – Case Study in Poland,2020.0,Real Estate Management and Valuation,K. Szopińska; M. Krajewska; J. Kwiecień,"Abstract Noise as a form of environmental pollution negatively affects the attractiveness of residential areas. Its adverse effect is particularly noticeable in the vicinity of roads with high traffic volume. Along with the dynamic development of roads, the problem of the impact of traffic noise on the prices of residential real estate is now common. The purpose of this article was to determine the impact of road traffic noise from a busy route on residential property prices using strategic noise map (SNM) resources as a source of information on real estate. Housing estate was identified in the area of the Polish city of Bydgoszcz. Assuming that the prices of real estate reflect its characteristics, the article attempts to verify the following hypothesis: for residential premises located at Kujawska St., noise is a feature that reduces their price. SNM resources, statistical analyses and econometric modelling were used in the research. The studies show that housing prices vary with acoustic zones, but differences in prices in the areas surveyed are not statistically significant. In addition, the undertaken research has shown that SNM resources can be a source of information on real",16.0,https://www.sciendo.com/pdf/10.1515/remav-2020-0013 8896b8e1a8e37d8a7e07a91418ec772fcc9fa6ca,10.1080/21681376.2023.2186805,"House prices, airport location proximity, air traffic volume and the COVID-19 effect",2023.0,"Regional Studies, Regional Science",T. Ngo; G. Squires; M. McCord; D. Lo,"ABSTRACT Although house prices and airports are influenced by distinct factors that shape their evolutions, they are also intrinsically connected through the natural and built environment. Standard theory suggests that air-traffic noise and proximity to key economic hubs such as airports are of prime importance to house prices and the housing market. This study contributes to understanding the link between the housing market, airport location proximity and air traffic. The research investigates this association across four key urban areas within New Zealand proximal to an international airport: Auckland, Wellington, Christchurch and Queenstown. Applying a generalized least squares (GLS) regression approach, the analysis reveals that house prices, air-traffic activity and proximity to airports within New Zealand demonstrate a statistically significant effect, and that air traffic volume has a positive effect on house prices. Moreover, the findings reveal a ‘U’-shape relationship between distance to the airport and house prices, suggesting that airport noise and pollution adversely affect house prices, with this effect diminishing with distance, indicating that economic influences an",16.0,https://www.tandfonline.com/doi/epdf/10.1080/21681376.2023.2186805?needAccess=true&role=button b29238c6ac3c3f5bf6f6ff8ea7a53ffbff776275,10.5379/urbani-izziv-en-2014-25-supplement-008,"A second wave of gentrification: The case of Parkhurst,",2014.0,,Paul Tsietsi Monare; T. McKay; Nico Kotze,"As in many other countries, gentrification and urban regeneration occurs across South Africa. Despite this, geographical research on gentrification in South Africa is limited. This study adds to the literature by presenting the case of Parkhurst, a Johannesburg surburb. The study found that Parkhurst displays numerous characteristics of gentrification. The housing stock has undergone extensive physical improvement, with almost one third renovated or under renovation. Property values have increased and the original residents have been displaced. Parkhurst has a demographic and a socio-economic profile typical of a gentrified suburb. It is populated by young, white, educated, weathy professionals people. In addition, gentrification has resulted in the conversion of residential into commercial space, with some residences converted into business premises. The result is a distinct linear commerical zone within the neigbourhood. Gentrification is also associated with a change in housing tenure from rentals to ownership, it was found that ownership was a common feature of Parkhurst. Overall, it was concluded that gentrification of the suburb has occurred over an extensive period of time a",16.0,https://doi.org/10.5379/urbani-izziv-en-2014-25-supplement-008 8c71ed2a01b3971e94d200fbf3065a0da0495751,10.1016/j.ribaf.2017.01.009,The Housing Market and the Credit Default Swap premium in the UK Banking Sector: A VAR Approach,2017.0,,Nadia Benbouzid; S. Mallick; Keith Pilbeam,"In the wake of the recent global financial crisis, this paper investigates the determinants of the Credit Default Swap premium in the UK banking sector for the period January 2004–April 2011. Employing a VAR model, we focus on the roles played by house prices, the yield spread, the UK TED spread and the FTSE 100 index. Our main results suggest that the CDS premium significantly increases in the medium term following a positive shock to the house price index, reflecting that continued house price appreciation can hide the likelihood of default risk, as shown in the insignificant response in the short run. We also find that a positive shock to the CDS premium significantly reduces house prices because it induces banks and other financial institutions to lend less, reducing the demand for housing and exerting further downward pressure on house prices. While a positive shock to stock prices lowers the CDS premium, a positive shock to the liquidity premium increases the CDS premium. Finally, our variance decomposition analysis shows that the house price shock explains over 19% of the long-run forecast-error variance of the CDS premium, while shocks in other variables each explain less t",16.0,https://doi.org/10.1016/j.ribaf.2017.01.009 0b745b20de584fd652f4d7c06d66d9345ee2d178,10.2139/ssrn.3891583,Macroprudential Policy Analysis via an Agent Based Model of the Real Estate Sector,2021.0,Social Science Research Network,Gennaro Catapano; F. Franceschi; M. Loberto; V. Michelangeli,"In this paper, we extend and calibrate with Italian data the Agent-based model of the real estate sector described in Baptista et al., 2016. We design a novel calibration methodology that is built on a multivariate moment-based measure and a set of three search algorithms: a low discrepancy series, a machine learning surrogate and a genetic algorithm. The calibrated and validated model is then used to evaluate the effects of three hypothetical borrower-based macroprudential policies: an 80 per cent loan-to-value cap, a 30 per cent cap on the loan-service-to-income ratio and a combination of both policies. We find that, within our framework, these policy interventions tend to slow down the credit cycle and reduce the probability of defaults on mortgages. However, with respect to the Italian housing market, we only find very small effects over a five-year horizon on both property prices and mortgage defaults. This latter result is consistent with the view that the Italian household sector is financially sound. Finally, we find that restrictive policies lead to a shift in demand toward lower quality dwellings. JEL Classification: D1, D31, E58, R2, R21, R31.",16.0,https://doi.org/10.2139/ssrn.3891583 bfa947c54509fd5e79a8e4ef3202247df862b071,10.1016/j.aap.2022.106825,"Spatial influence of engineering construction on traffic accidents, a case study of Jinan.",2022.0,Accident Analysis and Prevention,Zhen-Hua Mou; C. Jin; Hanbing Wang; Yi-Qun Chen; Ming Li; Yan-Yan Chen,"Due to urban construction, engineering transport vehicles are gradually increased on roads, which might speed up traffic accident risks. To investigate the influence of urban construction on traffic accidents, this paper adopted 1977 traffic accidents of engineering transport vehicles and 220 engineering construction projects for correlation analysis. First, considering three degrees (Major, Ordinary and Minor) of accidents, the spatial autocorrelation test of engineering transport vehicle accidents is carried out by using spatial econometric. Then to further evaluate and analyze the spatial regression model, the optimal model is selected to analyze the spatial influence of the floor area of different types of engineering construction projects on the accidents of engineering transport vehicles. The results show that the accident of engineering transport vehicles itself is spatially dependent, that is, the higher the severity of the accident, the more concentrated it is in space, and there is a significant spatial positive correlation with engineering construction projects. And the floor areas of synthetic land, residential land, commercial land and land for roads and transportation",16.0, a0bb06cac1a04d3884edd314c1d07d8f1e86b05b,10.1515/remav-2020-0028,Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach,2020.0,Real Estate Management and Valuation,Changro Lee; K. K. Park,"Abstract Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest a standard feed-forward network with two hidden layers, and show that it is sufficiently reasonable to apply its prediction to real-world projects such as property valuation. In addition, we propose a Bayesian neural network for describing uncertainty in house price predictions while providing a means to quantify uncertainty for each prediction. We choose Gangnam-gu, Seoul for the analysis, and predict house prices in the area using both networks. Although the Bayesian neural network did not perform better than the conventional network, it could provide a tool to measure the uncertainty inherent in predicted prices. The findings of this study show that a Bayesian approach can model uncertainty in property valuation, thereby promoting the adoption of deep learning tools in the real estate industry.",16.0,https://www.sciendo.com/pdf/10.1515/remav-2020-0028 e25664df9de1f78dcde6595e7a7d834098ae3ff8,10.1108/ijhma-08-2018-0062,Determinants of housing prices in Baku: empirical analyses,2019.0,International Journal of Housing Markets and Analysis,Khatai Aliyev; Mehin Amiraslanova; N. Bakirova; Narmin Eynizada," Purpose This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic. Design/methodology/approach Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares. Findings Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas. Research limitations/implications The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered. Practical implications Estimation results can be used for housing valuation by real estate market participants and investors. Social implications Research findings reveal importance of ",16.0, daab17a59b6b564782c7295508f3600607394535,10.5296/rae.v6i3.6171,Supply Side Aspects of Residential Housing for Low Income Earners in Kenya,2014.0,,Gayline Vuluku; J. Gachanja,"The need to have shelter is indispensable to humanity. Access to adequate and decent housing units decreases with the increase in population, scarcity of fixed assets like land, escalating house prices and the rising cost of living. The study uses time series data, 1980-2011, drawn from the Kenya National Bureau of Statistics to determine the factors that would be used to stimulate the low cost housing market in Kenya. The Instrumental Variable estimation technique is applied for this study using location specific factors for Mombasa and Nairobi. Regression results indicate unique results for the two towns. Whereas Nairobi had the lending rate, the cost of supply, the plinth area, labour and input cost index, inflation and the real interest rate being important in determining supply Mombasa reported the coefficient, cost of building a house, commercial bank rate and their lagged values as important factors in determining supply. At the same time inflation, treasury bill rate and plinth area were important in explaining supply of residential housing.",16.0,https://www.macrothink.org/journal/index.php/rae/article/download/6171/5218 a51e202ab5bec186bc975349250b3da1b6ecab75,10.1016/j.dib.2015.11.027,Real estate market and building energy performance: Data for a mass appraisal approach,2015.0,Data in Brief,P. Bonifaci; Sergio Copiello,"Mass appraisal is widely considered an advanced frontier in the real estate valuation field. Performing mass appraisal entails the need to get access to base information conveyed by a large amount of transactions, such as prices and property features. Due to the lack of transparency of many Italian real estate market segments, our survey has been addressed to gather data from residential property advertisements. The dataset specifically focuses on property offer prices and dwelling energy efficiency. The latter refers to the label expressed and exhibited by the energy performance certificate. Moreover, data are georeferenced with the highest possible accuracy: at the neighborhood level for a 76.8% of cases, at street or building number level for the remaining 23.2%. Data are related to the analysis performed in Bonifaci and Copiello [1], about the relationship between house prices and building energy performance, that is to say, the willingness to pay in order to benefit from more efficient dwellings.",16.0,https://www.sciencedirect.com/science/article/pii/S2352340915003224/pdf 511d291ed45bea7745e5e6b0311ed984ae280b38,10.1029/2017wr022133,Policy Targeting to Reduce Economic Damages From Land Subsidence,2018.0,Water Resources Research,Christopher M. Wade; K. Cobourn; G. Amacher; E. Hester,"Groundwater pumping contributes significantly to land subsidence, which generates economic costs as changes in the frequency of flooding affect property values. We evaluate these costs by estimating the marginal damages from pumping, which define the corrective policy incentive to address land subsidence externalities. In an application to the southern Chesapeake Bay region of Virginia, we find that land subsidence due to groundwater pumping is greatest in inland rural areas, but that the damages from pumping are greatest in coastal urban areas. Our results suggest that cost‐minimizing groundwater policy is spatially heterogeneous and targets the damages from land subsidence due to pumping in different locations, not simply physical land subsidence itself. These results are driven by differences in the spatial patterns of aquifer characteristics, which determine subsidence, and housing density, which determines the damages from subsidence.",16.0,https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2017WR022133 9be46ac55c23a12c57219a4fdfbaa73753d4b43f,10.3390/su8111082,A Vector Auto Regression Model Applied to Real Estate Development Investment: A Statistic Analysis,2016.0,,Fengyun Liu; S. Matsuno; R. Malekian; Jin Yu; Zhi-Xiong Li,"This study analyzes the economic system dynamics of investment in real estate from mainly four participants in China. Local governments limit the supply of commercial and residential land to raise fiscal revenue, and expand debts by land mortgage to develop industrial zones and parks. Led by local government, banks and real estate development enterprises forge a coalition on real estate investment and facilitate real estate price appreciation. The above theoretical model is empirically evidenced with VAR (Vector Auto Regression) methodology. A panel VAR model shows that land leasing and real estate price appreciation positively affect local government general fiscal revenue. Additional VAR models find that bank credit in addition to private and foreign funds respectively have strong positive dynamic effects on housing prices. Housing prices also have a strong positive impact on speculation from private funds and hot money.",16.0,https://www.mdpi.com/2071-1050/8/11/1082/pdf?version=1477390897 2adaf0a9c21f1fe4b1addbe9aa420bf9fcf05e64,10.1145/3371425.3371454,An intelligent automatic valuation system for real estate based on machine learning,2019.0,"International Conferences on Artificial Intelligence, Information Processing and Cloud Computing",Jiafei Niu; Pei-Qing Niu,"The demand for intelligent automatic real estate evaluation system is growing rapidly with the growth of the economy and the promotion of real estate marketization. House price is affected by a lot of factors from every aspect, how to deal with the insufficient multi-dimensional data is a great challenge. Besides, with the development of the artificial intelligence, applying machine learning and deep learning technology in the real estate valuation field is becoming more and more a tendency. This paper proposes a high-accuracy real estate valuation system based on machine learning. Concretely, the system is composed of three parts: 1) the repeated record recognition model to solve the problem of repeated house records collected from different web crawlers. 2) feature extraction and quantification module to deal with various types of characteristic factors. 3) automated evaluation module based on machine learning. Specially, we use the ensemble learning to integrate different machine learning models in order to improve the model performance further. And the above three parts are integrated into the intelligent automatic valuation system for real estate. The experiments on our self-c",16.0, e39c02bd7ee6e2bf24c8e0ba66e040041a5c278d,10.1111/1540-6229.12401,The price effects of greening vacant lots: How neighborhood attributes matter,2022.0,Real Estate Economics,Desen Lin; Shane T. Jensen; Susan M. Wachter,"We identify the effects of greening vacant lots on nearby housing prices and show how neighborhood attributes matter to these outcomes. Using data from a longstanding program in Philadelphia, we find that prices for houses within 1,000 feet of a greened vacant lot rise by about 4%, consistent with the literature, with the effect size increasing over time. Using the extensive data available in Philadelphia, we show how these effects vary by the attributes of the neighborhood in which they occur, with larger effects in areas with a high share of vacant land and higher‐than‐average median household incomes, with peak responses estimated at 19% and 15%, respectively. We demonstrate the importance of sample selection bias adjustment for identification of the effect of vacant lot greening.",16.0,https://doi.org/10.1111/1540-6229.12401 9bc38ce8797c172765a5f0dff02d716fa6b5c12e,10.1016/j.jfds.2020.04.001,Detection of rare events: A machine learning toolkit with an application to banking crises,2019.0,Journal of Finance and Data Science,J. Coffinet; Jean-Noël Kien,"Abstract We propose a machine learning toolkit applied to the detection of rare events, namely banking crises. For this purpose, we consider a broad set of macroeconomic series (credit-to-GDP gap, house prices, stock prices, inflation rates, long-term and short-term interest rates, etc.), in combination with their leads and lags, various filtering methodologies, and datascience models that complement time series analysis. The main advantages of the approach are its robustness, its flexibility and its prediction performance. Based on the best model specification, our methodology allows to compute an indicator for the probability of banking crisis along with an alert threshold up to 6 quarters ahead in real time for various developed economies.",16.0,https://www.sciencedirect.com/science/article/pii/S2405918820300015/pdf 4fb3415a79d125620d1fb2c161b4ca252360ba83,10.18070/erciyesiibd.801319,The Macroeconomic Determinants of The Housing Prices in Turkey,2021.0,,R. Akkay,"In this study, we examine the relationship between housing prices and its macroeconomic determinants in the Turkish economy over the period 2010-2020. The ARDL bounds testing to cointegration and Granger Causality test procedures are employed in the empirical part of the study. The empirical results show that there is a significant negative relationship between housing interest rates and housing prices. Besides, it was observed that the appreciation of the US dollar against the Turkish Lira and the increase in the employment level had an increasing effect on housing prices. As a result of our research, we conclude that country-specific facts affect the macroeconomic determinants’ ability to explain the changes in housing prices.",16.0,https://dergipark.org.tr/en/download/article-file/1314287 276215c6c1107e3ee9cb70edbb44186e634b0e4e,10.1016/j.physa.2021.125957,Group travel behavior in metro system and its relationship with house price,2021.0,Physica A: Statistical Mechanics and its Applications,Kanglin Zhu; Haodong Yin; Yun-Chao Qu; Jian-Jun Wu,"Abstract Modeling travel behavior inter-relationship is important to traffic planning and management as well as spread modeling of infectious disease and information. Our ability to understand inter-traveler behavior has remained unsatisfactorily limited due to the lack of empirical travel-together data. Taking advantage of access to a large-scale smart-card dataset from the Beijing metro network, we investigate a KS test-based method to identify group travel and study the spatial–temporal distribution of group travel as well as their interaction with socio-economic attributes. Origin and destination stations, departure time, and travel date are separately classified into several types according to the passenger flow adopting a tensor factorization technique. The results suggest that temporally passengers tend to travel in groups in the afternoon and on the weekend; spatially areas with moderate house prices are the least source for group demands, while areas with a relatively low house price are the most favorite place for group trips. This novel discovery can help to understand the impact of socio-economic attributes on intra-urban group/passenger flow movement.",16.0, bc28bd6b3037fce40dafd6a8a13a9e24de4a55f1,10.2139/ssrn.3414380,American Dream Delayed: Shifting Determinants of Homeownership,2019.0,Social Science Research Network,Natalia Khorunzhina; Robert A. Miller,"This paper develops and estimates a dynamic model of discrete choice for labor supply, fertility and transition from tenant to homeowner, to investigate the secular decline in homeownership over the past several decades, wholly attributable to households postponing the purchase of their first home. House prices only partly explain the decline; higher base level wages led to lower fertility also contributing to the decline, because households with children are more likely to own a home than those without. Somewhat surprisingly we find higher levels of female education ameliorated this trend, highly educated women placing greater value on homeownership.",16.0,https://doi.org/10.2139/ssrn.3414380 5a417a0b487d4c73da3ce68d2f114a1f9cad1c44,10.1016/j.habitatint.2019.04.001,"Tourism and housing prices in Santa Marta, Colombia: Spatial determinants and interactions",2019.0,Habitat International,Nestor Garza; Maria Carolina Ovalle,"Abstract We analyze Real Estate effects of Tourism-Oriented Development in Santa Marta, a touristic Caribbean city. We hypothesize that non-local demand affects the spatial distribution of housing prices and their determinants, for both –high and low-income groups. This is the first spatial economic analysis of the relationship between Tourism-Oriented Development and Real Estate Markets in a Global South city. We use 2016 housing transaction data to identify price determinants for different income groups using Spatial Quantile Regression. The regression results go beyond describing differentiated submarkets by income group, more common in the literature, identifying predicted prices by income groups in contiguous locations. These results are refined using qualitative information that confirms that Tourism-Oriented-Development affects the spatial distribution of prices, while increasing the everyday transport hardship in the urban agglomeration.",16.0, b633c49b2f5879640f919bdc54ae4390ee31d91d,10.1007/s11146-019-09718-8,Time-Geographically Weighted Regressions and Residential Property Value Assessment,2019.0,Journal of real estate finance and economics,Jeffrey P. Cohen; Cletus C. Coughlin; Jeffrey E. Zabel,,16.0,https://s3.amazonaws.com/real.stlouisfed.org/wp/2019/2019-005.pdf 11ebddb27aeaef3d08cf7a6dac9b7dd21eab0521,10.1142/s1793351x17500015,Home Price Index: A Machine Learning Methodology,2017.0,International Journal of Semantic Computing,Joseph R. Barr; E. A. Ellis; A. Kassab; C. Redfearn; Nani Narayanan Srinivasan; Kurtis B. Voris,,16.0, 1ba525356156eccbb1679920bb914cb6892f627c,10.59490/abe.2013.2.488,"Housing, Urban Renewal and Socio-Spatial Integration. A Study on Rehabilitating the Former Socialistic Public Housing Areas in Beijing",2013.0,Architecture and the Built Environment,X. Hui,"The initiative of this study derived from my concern of two critical urban issues in China today: housing and urban renewal. In the recent two decades, the Chinese urban housing stock underwent a significant, if not extreme, transformation. From 1949 to 1998, the urban housing stock in China largely depended on the public sector, and a large amount of public housing areas were developed under the socialistic public housing system in Beijing and other Chinese cities. Yet in 1998, a radical housing reform stopped this housing system. Thus, most of the public housing stock was privatized and the urban housing provision was conferred to the market. The radical housing privatization and marketization did not really resolve but intensified the housing problem. Along with the high-speed urbanization, the alienated, capitalized and speculative housing stock caused a series of social and spatial problems. The Chinese government therefore attempted to reestablish the social housing system in 2007. However, the unbalanced structure of the Chinese urban housing stock has not been considerably optimized and the housing problem is still one of the most critical challenges in China. Based on the ",15.0,http://dx.doi.org/10.59490/abe.2013.2.488 bbb8674354e766e47727d9838744cf79f968dae1,10.1108/ijhma-02-2022-0025,Explainable housing price prediction with determinant analysis,2022.0,International Journal of Housing Markets and Analysis,Ean Zou Teoh; Wei-Chuen Yau; T. Ong; Tee Connie," Purpose This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices. Design/methodology/approach Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R2 of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multino",15.0, e0e8c9dab646ac37bfb003fa62b83343f2b91a35,10.1108/jerer-03-2021-0015,"What effect does gun-related violence have on the attractiveness of a residential area? The case of Stockholm, Sweden",2021.0,Journal of European Real Estate Research,Mats Wilhelmsson; V. Ceccato; Manne Gerell,"PurposeThis study aims to analyse the effect of gun-related violence on housing values, controlling for the area's crime levels and locational factors. Previous studies that aimed to find a causal connection between crime and housing values used instrument variables to solve the endogeneity problem. Here, the authors have instead been able to take advantage of the fact that shootings have occurred in random time and space. This has made it possible to estimate models to create windows around the shooting (event) and to estimate the causal effects of the shootings. Thus, the authors aim to contribute to the regression discontinuity design method in this context to estimate the short-term effects.Design/methodology/approachUsing the regression discontinuity design method, the authors can estimate the short-term effects of shootings.FindingsFindings from the analysis indicate that shootings directly affect those who are impacted by shootings and indirectly affect the environments where shootings occur. The indirect effect of shootings is momentary as it is capitalised directly in housing values in the immediate area. The effect also appears to be relatively long-term and persistent as",15.0,https://doi.org/10.1108/jerer-03-2021-0015 e4725b150170cc847fbcdcc8cc68f54dd6784886,10.1016/j.jenvman.2023.119208,Linking G2SFCA method and circuit theory to promote spatial equity and landscape connectivity in urban ecological infrastructure.,2023.0,Journal of Environmental Management,Long-Yang Huang; Yu Lu; Jing Wang,"Protecting and constructing urban ecological infrastructure (UEI) is an important spatial means of improving the quality of life of urban residents, enhancing the benefits of the urban environment, and improving urban habitats. Landscape connectivity is the basis for UEI to play an ecological role. Spatial equity is an important goal for UEI to enhance urban resident happiness and sense of access and achieve environmental justice. Taking Yantai city as an example, this study analyzed the UEI morphological spatial pattern based on morphological spatial pattern analysis and calculated the spatial accessibility of UEI based on the gravity -based two-step-floating catchment area (G2SFCA) method. In addition, the study proposed the concept of accessible UEI and reflected the spatial differences in the equity of accessible UEI in urban residential areas through equity modeling that was oriented to connectivity enhancement and equity improvement. Moreover, the study integrated the location-allocation model and circuit theory to optimize the spatial layout of the existing UEI and construct an urban ecological corridor that considered landscape connectivity and leisure and recreational conv",15.0, ca65b47e4a32a7fbf3dbff68b5d51da1c7d13264,10.3390/land10111144,"Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China",2021.0,Land,Ruixi Dong; Feng-Ying Yan,"Urban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological features of space, and the dynamic characteristics of urban spatial structure reflect intra-city functional linkages. With the continuous agglomeration of population and industries; megacities have become the core spatial carriers leading China’s social and economic development; and their urban spatial structure has also been reconstructed. However; there is still a certain lack of understanding of the characteristics of the spatial structure of China’s megacities. This study aimed to reveal characteristics of the spatial structure of Chinese megacities at different scales using jobs-housing big data. To achieve this goal, spatial autocorrelation and a geographically weighted regression (GWR) model were applied to reveal static polycentricity, and community detection was used to reveal dynamic commuting communities. The distribution of jobs in urban space and jobs–housing balance levels in c",15.0,https://www.mdpi.com/2073-445X/10/11/1144/pdf?version=1635334005 6000df5e30e4cef0f63c1545ebe1d0434ba35232,10.3390/su12197928,The Impact of Airport Proximity on Single-Family House Prices—Evidence from Poland,2020.0,Sustainability,M. Bełej; R. Cellmer; Michał Głuszak,"Airports in Poland are obliged to observe the sustainable development principle and therefore to reduce their environmental impact by creating so-called limited use areas (LUA) related to aircraft-generated noise. The research authors analyzed airports’ impact on the prices of single-family homes located in the vicinity of airports. The LUA is therefore defined as the area designated to study the airport’s specific impact on the single-family housing market. This is a formal limit which determines the examination of price changes and the decision-making conditions of market participants. This methodical approach is justified because no excessive noise is expected outside the LUA. Therefore, two markets in the vicinity of airports were examined. One is in an LUA which is closer to the airport, and the other market is outside the LUA where external noise effects are not present. Thus, we consider that real estate located outside the LUA is not subject to a significant negative impact from the airport. The study covered the Gdańsk Lech Walesa Airport and the Warsaw Chopin Airport in Poland in adjacent areas with the research time horizon of 2013–2017. The study examined single-family ",15.0,https://www.mdpi.com/2071-1050/12/19/7928/pdf?version=1600961267 cad70754fe6402cfbd7c22bd2b1bbe6993bd0ca2,10.3390/su132413635,Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index,2021.0,Sustainability,S. Carbonara; Marco Faustoferri; Davide Stefano,"Urban quality, real estate values and property taxation are different factors that participate in defining how a city is governed. Real estate values are largely determined by the characteristics of urban environments in which properties are located and, thus, by quality of the location. Beginning with these considerations, this paper explores the theme of urban quality through a study of property values that seeks to define all physical (and thus measurable) characteristics that participate in defining urban quality. For this purpose, a multiple linear regression model was developed for reading the residential real estate market in the city of Pescara (Italy). In addition to the intrinsic characteristics of a property (floor area, period of construction/renovation, level, building typology and presence of a garage), input also included extrinsic data represented by the Urban Quality Index. Scientific literature on this theme tells us that many independent variables influence real estate prices, although all are linked to a set of intrinsic characteristics (property-specific) and to a set of extrinsic characteristics (specific to the urban context in which the property is located) ",15.0,https://doi.org/10.3390/su132413635 4f85c7691946cca9f3d1c4819140d03bfeb90764,10.33612/diss.133465113,Subjective Well-Being in a Spatial Context,2020.0,,R. Rijnks,"Recent progress in economics has moved subjective well-being towards the centre of the discourse on economic policy and academic research. Until recently, the spatial side of subjective well-being has remained under-exposed in the literature. This thesis aims to contribute to the literature on subjective well-being by placing a central focus on the spatial nature of these processes. We broadly contribute in three ways: first, we analyse which factors are associated with higher or lower subjective well-being within the region, using spatial data and spatial methods; second, we investigate how spatial differences in subjective well-being are associated with the residential location decision; finally, we investigate what the spatial extent is of these processes. We find that a person's social position within a neighbourhood is a key determinant of subjective well-being, while neither levels nor changes in accessibility of amenities correspond to an individual's subjective well-being. Local differences in quality of life can be used to explain both migration and property prices. For property prices, both individual differences in subjective well-being as well as happier neighbourhoods ",15.0,https://pure.rug.nl/ws/files/133465115/Title_and_contents.pdf eac951d44e1f60b2458df871dbe61c3b71660f42,10.3390/land11060860,"Walking Accessibility to the Bus Stop: Does It Affect Residential Rents? The Case of Jinan, China",2022.0,Land,Qingchun Liu; Peixiong Zhao; Yan Xiao; Xin Zhou; J. Yang,"It is a crucial question to understand the relationship between public transit and residential rents for the proposal of a sustainable transportation system and efficient allocation of lands during the policy marking process. Little has been discovered in the current literature regarding the impact of the bus system on residential rents. This study investigated walking accessibility to the bus stop based on the average daily on-ridership data of bus stops and street networks in Jinan, China, and analysed the impact on the spatial differentiation of residential rents using the spatial autocorrelation analysis and the geographically weighted regression (GWR) method. Our results suggested that residential rent levels in Jinan had evident spatial dependence and spatial differentiation characteristics, which was signified by a significant high rent, and a high accessibility distribution pattern surrounding both city and sub-city centres. GWR results further showed that walking accessibility to the bus stop could significantly improve residential rents. On the spatial scale, a 1% increase in walking accessibility could result in a premium of up to 0.427% and a 2.984% decline in rental pr",15.0,https://www.mdpi.com/2073-445X/11/6/860/pdf?version=1654594697 3e636739df1d0b6475edf2dcaa97b6bcf407a7b1,10.1108/ijhma-05-2022-0073,Heuristics and cognitive biases in the housing investment market,2022.0,International Journal of Housing Markets and Analysis,A. Cascão; Anabela Quelhas; A. M. Cunha," Purpose This paper aims to analyze the heuristics and cognitive biases described by behavioral finance in the investment decision-making process of Portugal’s housing market. Design/methodology/approach In a first step, the authors applied an exploratory factor analysis (EFA) to assess the impact of heuristics and cognitive biases on investors’ decision-making. In a second step, the authors run a structural equation model (SEM) diagram path to assess if the sociodemographic characteristics of housing market investors determine the identified heuristics and if the heuristics condition the investors’ investment criteria. Findings Herd behavior and the heuristics of representativeness, availability and anchoring influence the housing market’s investors’ behavior in their decision-making process. Investors with above-average income show higher levels of overconfidence. Investors showing higher levels of overconfidence also tend to be more sensitive to the house price under analysis for investment. Women tend to show higher levels of the availability and anchoring heuristic. In turn, housing market investors showing higher levels of availability and anchoring heuristic tend to be m",15.0, fa36d1e26273070a8e82365bf9ebb49f8d7a8042,10.3390/su11133681,The Impact of High-Speed Rail on Housing Prices: Evidence from China’s Prefecture-Level Cities,2019.0,Sustainability,Rong Wang; Li-Xin Ye; Liwen Chen,"The rapid development of the high-speed rail (HSR) network enhanced the regional accessibility between cities, drove the rise in cities’ investment levels, and expanded the activity radius of the labor force, causing changes in housing prices along the rail lines. Based on panel data of 285 cities in China from 2008–2016, this study used the difference-in-difference based on propensity score matching (PSM-DID) method to calculate the impact of HSR on housing prices. The conclusions of the study indicated that, at the regional level, HSR significantly promoted the rise in housing prices in HSR cities along the rail line. HSR had a positive effect on housing prices, where the coefficient of HSR influence was 0.1511 and passed a 1% significance test. From the perspective of the combination of sub-regional and sub-city scales, HSR mainly played a significant role in promoting housing prices in “small and medium-sized cities” and “central and western cities”, especially in small and medium-sized cities in the central and western regions; in general, HSR can narrow the housing price gap between “small and medium-sized HSR cities” in the central and western regions and large HSR cities in",15.0,https://www.mdpi.com/2071-1050/11/13/3681/pdf?version=1562238470 3b37941a7cbd283073b7d78d9cbe2e0afe700cde,10.1186/s40537-018-0154-3,A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques,2018.0,Journal of Big Data,Stephen Clark; N. Lomax,"Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commercial organisations and local and national governments in understanding this market. This case study uses data that are supplied from a property listings web site and are unique in their scale, with over 1.2 million rental property listings available over a 2 year period. The data is analysed in a large data institute using generalised linear regression, machine learning and a pseudo practitioner based approach. The study should be seen as a practical guide for property professionals and academics wishing to undertake such appraisals and looking for guidance on the best methods to use. It also provides insight into the property characteristics which most influence rental listing price. From the regression analysis, attributes that increase the rental listing price are: the number of rooms in the property, proximity to central London and to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. Of the machi",15.0,https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-018-0154-3 5febf2bd6e100aa1b4bc0517a4a54f617b5de8da,10.1111/1475-6773.13011,External Determinants of Veterans’ Utilization of VA Health Care,2018.0,Health Services Research,A. Hanchate; Austin B. Frakt; N. Kressin; Amal N. Trivedi; A. Linsky; H. Abdulkerim; Kelly L. Stolzmann; D. Mohr; S. Pizer,"OBJECTIVE Veterans' utilization of Veterans Affairs (VA) health care is likely influenced by community factors external to the VA, including Medicaid eligibility and unemployment, although such factors are rarely considered in models predicting such utilization. We measured the sensitivity of VA utilization to changes in such community factors (hereafter, ""external determinants""), including the 2014 Medicaid expansion following the Affordable Care Act. DATA SOURCES/STUDY SETTING We merged VA health care enrollment and utilization data with area-level data on Medicaid policy, unemployment, employer-sponsored insurance, housing prices, and non-VA physician availability (2008-2014). STUDY DESIGN For veterans aged 18-64 and ≥65, we estimated the sensitivity of annual individual VA health care utilization, measured by the cost ($) of care received, to changes in external determinants using longitudinal regression models controlling for individual fixed effects. PRINCIPAL FINDINGS All external determinants were associated with small but significant changes in VA health care utilization. In states that expanded Medicaid in 2014, this expansion was associated with 9.1 percent ($826 m",15.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1475-6773.13011 da487cbbadf24fb7b229f8c42b677863ddf48493,10.1080/01621459.2020.1817749,Do School Districts Affect NYC House Prices? Identifying Border Differences Using a Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs,2020.0,,M. Rischard; Zach Branson; L. Miratrix; L. Bornn,"Abstract What is the premium on house price for a particular school district? To estimate this in New York City we use a novel implementation of a geographic regression discontinuity design (GeoRDD) built from Gaussian processes regression (kriging) to model spatial structure. With a GeoRDD, we specifically examine price differences along borders between “treatment” and “control” school districts. GeoRDDs extend RDDs to multivariate settings; location is the forcing variable and the border between school districts constitutes the discontinuity threshold. We first obtain a Bayesian posterior distribution of the price difference function, our nominal treatment effect, along the border. We then address nuances of having a functional estimand defined on a border with potentially intricate topology, particularly when defining and estimating causal estimands of the local average treatment effect (LATE). We test for nonzero LATE with a calibrated hypothesis test with good frequentist properties, which we further validate using a placebo test. Using our methodology, we identify substantial differences in price across several borders. In one case, a border separating Brooklyn and Queens, we",15.0,https://figshare.com/articles/dataset/Do_School_Districts_Affect_NYC_House_Prices_Identifying_Border_Differences_Using_a_Bayesian_Nonparametric_Approach_to_Geographic_Regression_Discontinuity_Designs_/12912705 d87d953ba28a42279f5ee562c4a492d0165523ac,10.1007/s11769-016-0826-x,Spatio-temporal differentiation of residential land for coastal town: A case study of Dalian Jinshitan,2016.0,Chinese Geographical Science,Jun Yang; Jing Sun; Hong-Da Zhao; Jian-Chao Xi; Xueming Li,"Based on SPOT5 satellite remote sensing images and land use data in five time phases covering the period 1988–2012, the distribution characteristics, spatial differentiation processes, and driving mechanisms affecting the residential land of the Jinshitan Tourist Resort were studied. The work included a combination of GIS technology, adoption of the differentiation index D, and the multi-group differentiation-measure model D (m). The results provided a basis for the following conclusions: 1) From 1988 to 2012, the degree of differentiation of the rural residential land first decreased, and then increased. The general residential, commercial residential and garden-house land-types all showed declining trends, of which the garden-house type presented the most intense volatility. 2) The overall background of the traditional residential land type was gradually replaced by a new residential land type, and the total area of residential land increased year by year, significantly improving the overall residential conditions. The areas of the new residential land type showed growth, and were increasingly widely distributed in the coastal tourist resorts, transportation corridors, and scenic",15.0,http://ir.igsnrr.ac.cn/handle/311030/43360 852783167c2a9bd3159fbf8fc300680403f7d46f,10.5755/j01.ee.32.5.28057,Impact of the Economic Stimulus Measures on Lithuanian Real Estate Market under the Conditions of the COVID-19 Pandemic,2021.0,The Engineering Economist,Vaida Pilinkienė; A. Stundžienė; E. Stankevičius; Andrius Grybauskas,"The COVID-19 pandemic caused a number of challenges worldwide regarding not only the human health perspective, but also the economic situation. Quarantine, imposed in many countries, forced a substantial part of businesses to close or narrow down their activities, thus leaving corporations and employees without any or with lower income. If national governments had not undertaken any actions to save national economies, the consequences could have been even more devastating. The real estate market is an important part of economy. Instability in the real estate market can cause financial problems, vulnerability of population’s welfare and other negative effects. This research aims to assess the impact of the economic stimulus measures on the real estate market under the conditions of the COVID-19 pandemic in Lithuania. The research methods include comparative analysis, correlation analysis, stationarity test, regression analysis and the ARDL models. The results indicate that the economic stimulus measures only partially contribute to stabilization of the real estate market in Lithuania. The drop in housing prices was 2.9 percent lower because of the economic stimulus in the second qua",15.0,https://inzeko.ktu.lt/index.php/EE/article/view/28057/15175 bcb279d1639a77b9062dee2d658c909b375a80f2,10.1007/s10708-022-10811-4,Understanding the shared working spaces: a Geography of Co-working supply in Delhi,2022.0,GeoJournal,A. Tiwari,"The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity ",15.0,https://link.springer.com/content/pdf/10.1007/s10708-022-10811-4.pdf 3376bfc8f0cb463a6e5fa4ecc4cec1058103e352,10.3390/su10114178,Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data,2018.0,Sustainability,Yadi Zhu; Feng Chen; Ming Li; Zi-Jia Wang,"Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passenge",15.0,https://www.mdpi.com/2071-1050/10/11/4178/pdf?version=1542106130 9ccbe6e4fa710fd4273f6aa07a7df971efb4e1eb,10.1080/09613218.2023.2176284,How prebound effects compromise the market premium for energy efficiency in German house sales,2023.0,Building Research & Information,R. Galvin,"ABSTRACT Recent studies indicate that houses with higher energy efficiency usually have higher market prices, a ‘market premium for energy efficiency’. But in Germany the usefulness of this premium is confounded by the ‘prebound effect’: the gap between officially certificated energy ratings and actual energy consumption. Attempts have been made to close this gap from two complementary directions: downwards, by obtaining more accurate and less pessimistic technical estimates of idealised energy performance; and upwards, by estimating how much energy occupants realistically need for health and comfort. This study investigates prebound effects alongside an analysis of house prices in Germany, using a large database of house sale advertisements from 2007 to 2021, focusing on pre-1980 homes that were re-sold in 2019–2021. It uses ordinary least-squared multivariate regression to estimate market premiums for energy efficiency and sets these alongside estimates of prebound (and rebound) effects. It finds that prebound effects can lead purchasers to overestimate future energy savings and therefore pay more for properties than their actual worth. It also offers simple models to help purcha",15.0,https://www.tandfonline.com/doi/pdf/10.1080/09613218.2023.2176284?download=true f770dde0e515155a672ed0bd0a9805daf3c8434f,10.3390/su13116320,Data-Driven Analysis on Inter-City Commuting Decisions in Germany,2021.0,,Hui Chen; Sven P. Voigt; Xiaoming Fu,"Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, diff",15.0,https://www.mdpi.com/2071-1050/13/11/6320/pdf?version=1622647198 a3f49dffc296ee3889b44fecaf34a1400986c88c,10.3390/land11030413,Economic Valuation of Urban Green Spaces across a Socioeconomic Gradient: A South African Case Study,2022.0,Land,L. Lategan; Zene Steynberg; E. Cilliers; S. Cilliers,"Urban green spaces (UGSs) may present economic contributions through increases in proximate property values, encapsulated in the proximity principle (PP). More data on the PP is required from the Global South, where the quality and equitable distribution of UGSs are important considerations. This paper investigates the PP in Potchefstroom, South Africa following a quantitative approach, by statistically analyzing municipal property valuations in three districts differentiated according to their socioeconomic status (SES). Districts are divided into sample areas where three zones are demarcated according to their proximity to a UGS. The results show that property valuations are generally higher for properties in closer proximity to UGSs in lower- and higher-income samples, but are lower in middle-income areas. Neighborhood characteristics and SES, UGS amenity and maintenance, ecosystem services and disservices, domestic garden area and residential property size may be connected to the confirmation or rejection of the PP. The rejection of the PP in middle-income areas indicates a need to improve public UGSs as amenity destinations. The results confirming the PP in low-income areas co",15.0,https://www.mdpi.com/2073-445X/11/3/413/pdf?version=1647245017 4fa98502afe6b09c79926a26aec6753541ceb4bf,10.1111/1540-6229.12385,Continuation of air services at berlin‐tegel and its effects on apartment rental prices,2022.0,Real Estate Economics,P. Breidenbach; Jeffrey P. Cohen; S. Schaffner,"Berlin-Brandenburg airport (BER) became well-known far beyond German borders due to substantial planning problems and multiple opening delays. Originally planned to open in March 2012, BER finally opened in 2020, after seven delay announcements. Focusing on the two most surprising and meaningful announcements, these unexpected delays form an exogeneous shock for residents surrounding the largest existing airport, Berlin-Tegel, which was expected to close immediately upon opening of BER. We use these delay announcements as a quasi-experiment to analyze separately the effects on apartment rental prices of aircraft noise (due to arriving/departing flight paths) and airport proximity (accessibility). The results suggest there is a negative effect of aircraft noise on rental prices of 2% to 5%, while there are positive proximity effects from Berlin-Tegel of 1% to 3%. We consider heterogeneity using quantile regression and find the negative noise effects are larger for higher-priced apartments. We disentangle aircraft noise and other (environmental) noise effects, and herein find that aircraft noise lowers property values by 2% to 2.8% while properties facing additional environmental noi",15.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-6229.12385 c4de482d5c48a35197cf9d23d343a7385b3eb987,10.1109/icdm.2016.0134,House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis,2016.0,Industrial Conference on Data Mining,Bang Liu; B. Mavrin; Di Niu; Linglong Kong,"Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluati",15.0,https://arxiv.org/pdf/1803.00919 4231bb5ae9abf716a9a61de2188bb3f9e891924b,10.1016/j.jacr.2018.12.047,Sociodemographic Variation in the Use of Conservative Therapy Before MRI of the Lumbar Spine for Low Back Pain in the Era of Public Reporting.,2019.0,Journal of the American College of Radiology,K. Lind; J. Flug,"PURPOSE To evaluate the relationship between use of MRI of the lumbar spine for low back pain without prior conservative therapy and sociodemographic factors after the implementation of public reporting for Medicare's Hospital Outpatient Imaging Efficiency Measure for MRI Lumbar Spine for Low Back Pain (OP-8) metric. MATERIALS AND METHODS We conducted a secondary data analysis using a nationally representative sample of 2009 to 2014 Medicare claims to evaluate trends in use of conservative therapy before MRI of the lumbar spine. Continuously enrolled fee-for-service Medicare beneficiaries were included. We applied the same criteria used by Medicare to generate a measure consistent with OP-8. Regression was used to evaluate trends in OP-8 by reporting status (outpatient hospital or clinic) and beneficiary characteristics. Age, sex, and race from the Medicare denominator and area-level socioeconomic measures from the Area Health Resource File were used as covariates. RESULTS Use of conservative therapy before MRI increased regardless of OP-8 reporting status. Several sociodemographic characteristics were associated with the likelihood of receiving conservative therapy before MRI;",15.0, c9c1de74155edd8e89f5948ec680863e2515a63e,10.3390/land11091439,"Exploring the Spillover Effects of Urban Renewal on Local House Prices Using Multi-Source Data and Machine Learning: The Case of Shenzhen, China",2022.0,Land,Xiaojun Li; Jie-Yu Wang; K. Luo; Yuan Liang; Shaojian Wang,"Urban renewal is a current hotspot for research in the field of urban geography and urban planning. However, few studies have been able to quantify the impact of urban renewal on local house prices. Taking Shenzhen as an example, this paper measures the added premium effect of urban renewal on local house prices through econometric models and multi-source data and explores the spillover effect of urban renewal on house prices using an integrated model based on machine learning and Geo-detector analysis. The main findings are: (1) Shenzhen’s urban renewal had a significant positive premium effect on the unit transaction price of local housing. (2) The population characteristics and the accessibility to transport in the context of urban renewal are the main drivers for premiums on house prices. (3) There is spatial heterogeneity with respect to the housing premium effect due to urban renewal, among which optimization of the density of the road network is most closely associated with the premium effects. The interaction of the road density network and the population density of particular streets drives the medium premium effect. Our findings have important implications for refinements",15.0,https://www.mdpi.com/2073-445X/11/9/1439/pdf?version=1662029509 4245335f1aefe998e657ad55c78e37fa2c754dd4,10.1017/flw.2023.3,"Feeling comfortable with a mortgage: The impact of framing, financial literacy and advice",2023.0,Journal of Financial Literacy and Wellbeing,S. Thorp; Junhao Liu; Julie R. Agnew; H. Bateman; Christine Eckert; Fedor Iskhakov," The family home is the most important asset on household balance sheets, aside from human capital. Choosing a suitable mortgage is therefore critical to financial well-being but households often make costly mistakes. We collect data in an online survey to test borrowers’ comfort with, and understanding of, mortgage debt. We analyze the impact of financial literacy, mortgage broker advice and whether the loan is framed as a lump sum debt or an equivalent stream of repayments. We conjecture that participants’ comfort with loans and their ability to match lump sum debt to equivalent repayment streams will help them to choose a suitable mortgage. Results show that participants with high financial literacy are less comfortable with mortgage debt in general and also less sensitive to framing than those with low financial literacy. Literate participants are better able to match repayment streams with the equivalent lump sums. Endogeneity-controlled regression analysis shows that consulting brokers leads to higher comfort with debt and lower sensitivity to framing. Survey responses also indicate more uncertainty about future house prices among borrowers who intend to consult brokers than",15.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/CEF71B9B9C201102D100475E5700533F/S2753321223000035a.pdf/div-class-title-feeling-comfortable-with-a-mortgage-the-impact-of-framing-financial-literacy-and-advice-div.pdf a09cc5e41fe71832b0b3cccc50ef0a6dc0daa369,10.3389/fpubh.2021.816372,How Do Housing Prices Affect Residents' Health? New Evidence From China,2022.0,Frontiers in Public Health,Hui-Qin Wang; Liqi Liang,"This paper aims to explore the effect and mechanism of rising housing prices on residents' physical and mental health. Using data from the China Family Panel Studies from 2014 to 2018, we investigate the impact and mechanism of rising housing prices on the mental and physical health of urban residents through multiple grouping regression and analysis of variance. The study finds that overall, rising housing prices have a positive effect on residents' mental health but a negative effect on physical health, and those who do not own a house show the greatest adverse effect. The impact of rising housing prices on health is mainly reflected in three aspects: the wealth effect, cost effect, and comprehensive environmental expectation effect. Of these, the wealth effect and comprehensive environmental expectation effect play a role in promoting residents' health, whereas the cost effect has a strong inhibitory effect. This paper also analyzes how house prices impact health and finds that having health insurance reduces residents' active health behavior, thus affecting their physical and mental health levels, which has a positive effect on uninsured residents.",15.0,https://www.frontiersin.org/articles/10.3389/fpubh.2021.816372/pdf 7436237c9b1db9a0ab3ec43cba65261457bbe8d5,10.1007/s11146-020-09799-w,An Intra-City Analysis of House Price Convergence and Spatial Dependence,2020.0,Journal of real estate finance and economics,Omar Bashar,"This paper seeks to test the intra-city convergence of house prices using the comprehensive suburban house price and census data for a key Australian city. It applies the regression-based β-convergence test combined with spatial econometric models to examine the conditional convergence of house prices across 300+ suburbs in the Melbourne metropolitan area. This paper finds evidence of house price convergence when the econometric model controls for spatial effects and a few other suburban characteristics, such as population size, economic profile, education and employment profile, and crime rate. However, the convergence rate is found to be decelerated in the post-GFC periods. The estimation results suggest a faster convergence rate for units/apartments than for freestanding houses. There is also evidence of spatial effects, implying that house price growth in Melbourne suburbs is spilling over into the neighboring suburbs. These findings are expected to shed new light on understanding the residential real estate market and price dynamics within a city in the context of a developed nation and help design relevant housing policies.",15.0,https://figshare.com/articles/journal_contribution/An_intra-city_analysis_of_house_price_convergence_and_spatial_dependence/20688571 918fe518d6013d680b0997cbf6ef3ac3ce9bb229,10.1108/ijhma-06-2018-0047,Asymmetric sensitivities of house prices to housing fundamentals: evidence from UK regions,2019.0,International Journal of Housing Markets and Analysis,H. Alqaralleh," Purpose This paper aims to examine asymmetries in the house price cycle and to understand the dynamic of housing prices, incorporating macroeconomic variables at regional and country level, namely, housing affordability, the unemployment rate, mortgage rate and inflation rate. Design/methodology/approach To highlight significant differences in the asymmetric patterns of house prices between regions, the STAR model is adopted. Findings The authors highlight significant differences in the asymmetric patterns of house prices between regions, in which some areas showed asymmetric response over the housing cycle; here the LSTAR model outperforms other models. In contrast, some regions (the South West and the North West) showed symmetric properties in the tails of the cycle; therefore, the ESTAR model was adopted in their case. Practical implications Being limited to a few fundamentals, this study opens an avenue for further research to investigate this dynamic using in addition such demand-supply factors as land supply, construction cost and loans made for housing. These findings can also be used to examine whether other models such as ARIMA, exponential smoothing or artificial n",15.0, 4f7bdfe3afa5f09654b82462137a063663134086,10.1177/00420980231192232,Spillover of urban gentrification and changing suburban poverty in the Amsterdam metropolis,2023.0,Urban studies,H. Booi,"Suburbanisation appears to be reviving in the beginning of the 21st century. It has once again become an important force driving suburban growth. However, in contrast with 20th-century suburbanisation, the current phase might be better understood through the spillovers of urban gentrification and suburbanisation of poverty that are happening while the core city continues to grow. Using a multilevel binomial regression model on all moving households in the metropolitan region around Amsterdam, this paper shows that movers from Amsterdam are clearly urban oriented when moving out of the city. High-income households dominate the suburbanisation towards neighbourhoods near the city and to relatively urban residential neighbourhoods from the pre-war period. These are also neighbourhoods with sharp house price increases. This reveals a spillover of the urban gentrification process beyond the core city borders. Suburban in-migration of low-income households from the city has also increased and is more oriented to neighbourhoods where affordable housing is accessible.",15.0,https://journals.sagepub.com/doi/pdf/10.1177/00420980231192232 d88c6c59b59a9de7f364aec7702dac0e996728e8,10.1088/1742-6596/1802/3/032034,House price prediction using polynomial regression with Particle Swarm Optimization,2021.0,"Journal of Physics, Conference Series",Chenhao Zhou,"This paper combines the characteristics of the real Washington DC house price estate market and the theoretical model of housing prices to conduct an empirical analysis and comparison of the current mainstream housing price index prediction models. It is found that the current mainstream model only studies the trend of the housing price index itself, and is not sensitive to the characteristics of the house itself. Therefore, this paper uses a multiple regression model to integrate the advantages of external factors and use the improved housing price composition establishing a multiple regression prediction model with the particle swarm optimization. It not only makes up for the disadvantages of poorly determined housing price regression indicators and lack of statistical data in multiple regression prediction, but also enables the model to reflect the inflection point of housing prices in advance. PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction.",15.0,https://iopscience.iop.org/article/10.1088/1742-6596/1802/3/032034/pdf d68a1aafdfeca06629ad31c61bb928ef7eb1c36a,10.2478/remav-2022-0002,Training and Interpreting Machine Learning Models: Application in Property Tax Assessment,2022.0,Real Estate Management and Valuation,Changro Lee,"Abstract In contrast to the outstanding performance of the machine learning approach, its adoption in industry appears to be relatively slow compared to the speed of its proliferation in a variety of business sectors. The low interpretability of a black-box-type model, such as a machine learning-based valuation model, is one reason for this. In this study, house prices in Seoul and Jeollanam Province, South Korea, were estimated using a neural network, a representative model to implement machine learning, and we attempted to interpret the resultant price estimations using an interpretability tool called a partial dependence plot. Partial dependence analysis indicated that locally optimized valuation models should be designed to enhance valuation accuracy: a land-oriented model for Seoul and a building-focused model for the Jeollanam Province. The interpretable machine learning approach is expected to catalyze the adoption of machine learning in the industry, including property valuation.",15.0,https://www.sciendo.com/pdf/10.2478/remav-2022-0002 56b06abff89944c086296fb66bea306223c2f3e4,10.1080/10095020.2024.2343011,GWmodelS: a standalone software to train geographically weighted models,2024.0,Geo-Spatial Information Science,Binbin Lu; Yi-Gong Hu; Dongyang Yang; Yong Liu; Guangyu Ou; Paul Harris; Chris Brunsdon; A. Comber; Guan-Peng Dong,"ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.",15.0,https://doi.org/10.1080/10095020.2024.2343011 c4051a93ff40e9d744ed084bd8ecd707cc9da5b4,10.1016/s2212-5671(16)00027-7,Review the Issue of Housing among Urban Dwellers in Malaysia with Special Reference towards Affordability to Home Ownership,2016.0,,Hamidah Ramlan; Eleeza Eleena Zahari,"Abstract This study aims to review the issue of housing among urban dwellers in Malaysia with special reference towards the affordability of home ownership. We find that the monthly income has a significant relationship with a type of home using correlation test. There is a negative relationship between type of home and monthly income at the 1% significance level. It shows that monthly income has a relationship to profitability. The correlation test shows that house price and time taken to buy have a positive correlation. For the regression test, only monthly income no significant impact towards the home type of ownership. There is a significant impact between the home type of ownership with time taken to buy and house price at 1% significant level.",15.0,https://www.sciencedirect.com/science/article/pii/S2212567116000277/pdf 7b030e3e2ab48150e1fc58d4ba2af5c1b31c6118,10.2478/amns.2022.1.00052,Analysis and prediction of second-hand house price based on random forest,2022.0,Applied Mathematics and Nonlinear Sciences,Yan Zhang; Jin-Xin Huang; Jiahui Zhang; Shu-Yi Liu; Samer Shorman,"Abstract Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, a Random Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the model variables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict house prices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted by the real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value.",15.0,https://www.sciendo.com/pdf/10.2478/amns.2022.1.00052 27c764c633767d58d84501919117e8788fc57642,10.1016/j.jtrangeo.2020.102893,"Exploring residential relocation behavior for families with workers and students; a study from Beijing, China",2020.0,,Fei Xue; En-Jian Yao; Fang-Lei Jin,"Abstract The effects of residential relocation on changes in travel behaviors have been studied worldwide; however, little is known about factors that influence relocation behavior in households with workers and students. This study explores the relationship between residential relocation behavior and its factors, such as the built environment, the housing price, the work commute distance, the school commute distance, and households' sociodemographic attributes, by building a structural equation model using the data collected in the Fifth Travel Survey of Beijing Inhabitants. The results show that the built environment and sociodemographic attributes have direct effects on residential relocation behavior and the housing price, work commute distance, and school commute distance have indirect effects on it. Besides, trade-offs between the housing price and the built environment and between the housing price and the home-work and home-school commute distances are found. Based on the results, a number of suggestions for the balanced development of public transportation and city land-use plans are proposed.",15.0, 66f3b8fc39940e91d6eb5ca42830a78fe5b79b0a,10.1111/pirs.12063,Exploring scan methods to test spatial structure with an application to housing prices in Madrid,2013.0,Papers in Regional Science,F. López; C. Chasco; J. le Gallo,"We evaluate the usefulness of the spatial scan test in specification testing for spatial econometric models. The null hypothesis assumes equality of the mean values of a variable in all the locations of a geo‐referenced data set. The alternative hypothesis relies on the existence of one (or more) spatial cluster(s) where mean values differ from those of the rest of the sample. First, we conduct a Monte‐Carlo simulation study to analyse the properties of this test when applied to regression residuals. Second, we illustrate this test with an empirical application on housing prices in Madrid.",15.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/pirs.12063 991587ff922591a9b8f9ef0121b4411b68f421ba,10.1093/aler/ahy009,Hide or Show? Observability of Private Precautions Against Crime When Property Value is Private Information,2018.0,American Law and Economics Review,F. Baumann; Philipp Denter; Tim Friehe,"This paper analyzes a contest in which defenders move first, have private information about the value of the objects they are trying to protect, and determine the observability of their defense efforts. The equilibrium consistent with the intuitive criterion depends on the distribution of defender types, the magnitude of the difference between defender types, and the asymmetry between defender and aggressor regarding the valuation of the objects at stake in the contest. Our setting captures key characteristics of the interaction between households and thieves, focusing on the classic distinction between observable and unobservable private precautions against crime. An analysis of welfare implications determines that a setting in which information about the value of the protected objects is private results in a better outcome than a complete-information scenario.",15.0, 35b2e9b41a61855b661d26f5d0231fe4231d4694,10.1111/cico.12071,"Changing Neighborhood Determinants of Housing Price Trends in Southern California, 1960–2009",2014.0,,John R. Hipp; Amrita Singh,,15.0,https://escholarship.org/content/qt1gt4f4cx/qt1gt4f4cx.pdf?t=nm2xvz 8f5757703a010ac3f39fe05e4616e3d438640bce,10.1007/s12061-021-09383-6,"Identifying the Spatial Heterogeneity in the Effects of the Social Environment on Housing Rents in Guangzhou, China",2021.0,Applied spatial analysis and policy,Yang Wang; Kang-Min Wu; Lixia Jin; Geng-Zhi Huang; Yu-Ling Zhang; Yong-Xian Su; Hong'ou Zhang; Jing Qin,,15.0, 3f71af619b46cc835adae599f02bb33a5d8da119,10.1108/ijhma-05-2012-0020,Ripple effects of house prices: considering spatial correlations in geography and demography,2013.0,,Le Ma; Chunlu Liu,,15.0, cce28f52d1753b3c8515b61bbd8995005ed2865b,10.1007/s11116-021-10233-0,Evaluating the spatial and temporal distribution of beltway effects on housing prices using difference-in-differences methods,2021.0,Transportation,J. Murray; Eleni Bardaka,,15.0, 805b1f41a73870d0d17451e1c7328afce971915a,10.1007/s12061-023-09536-9,"Urban Structure, Housing Prices and the Double Role of Amenity: A Study of Nanjing, China",2023.0,Applied spatial analysis and policy,Meitong Liu; Y. Wei; Yangyi Wu,,15.0, 6fa90d237eb4b36ac555a43b3383f98e1789d26e,10.1016/j.lanepe.2022.100580,Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data.,2023.0,The Lancet Regional Health - Europe,J. Bennett; T. Rashid; Ali Zolfaghari; Y. Doyle; Esra Suel; Jonathan Pearson-Stuttard; Bethan Davies; D. Fecht; Emily Muller; Ricky Nathvani; Noémie Sportiche; H. Daby; Eric J. Johnson; Guangquan Li; S. Flaxman; M. Toledano; M. Asaria; M. Ezzati,"Background London has outperformed smaller towns and rural areas in terms of life expectancy increase. Our aim was to investigate life expectancy change at very-small-area level, and its relationship with house prices and their change. Methods We performed a hyper-resolution spatiotemporal analysis from 2002 to 2019 for 4835 London Lower-layer Super Output Areas (LSOAs). We used population and death counts in a Bayesian hierarchical model to estimate age- and sex-specific death rates for each LSOA, converted to life expectancy at birth using life table methods. We used data from the Land Registry via the real estate website Rightmove (www.rightmove.co.uk), with information on property size, type and land tenure in a hierarchical model to estimate house prices at LSOA level. We used linear regressions to summarise how much life expectancy changed in relation to the combination of house prices in 2002 and their change from 2002 to 2019. We calculated the correlation between change in price and change in sociodemographic characteristics of the resident population of LSOAs and population turnover. Findings In 134 (2.8%) of London's LSOAs for women and 32 (0.7%) for men, life expect",14.0,https://doi.org/10.1016/j.lanepe.2022.100580 385193670b0a77556d2633a857a7a83c459a955f,10.3390/app122010660,Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal,2022.0,Applied Sciences,M. McCord; D. Lo; P. Davis; John McCord; L. Hermans; P. Bidanset,"Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Fi",14.0,https://www.mdpi.com/2076-3417/12/20/10660/pdf?version=1666350660 3bb651ed2d7aa878f7ff88c9abdca16574b409dc,10.1177/016146812112300205,A Spatial Analysis on Charter School Access in the New York Metropolitan Area,2021.0,Teachers College Record,Jin Lee; C. Lubienski,"Background Extant literature has consistently indicated that access to charter school markets is shaped by social geography. Given interest in location shown by charter schools and parents, estimating potential spatial access to charter schools has become instrumental in understanding equal opportunities for charter school enrollment in metropolitan areas with preexisting residential segregation. Purpose By considering the increasing significance of sociogeography, this article asks whether students have equal opportunities for potential spatial access to charter schools across communities and how disparities in charter school access are related to housing patterns. Setting This study focuses on 122 charter schools in the New York metropolitan region, a highly segregated metropolitan area in the United States where charter schools are a primary component of education reform. Research Design The first part of this study illustrates patterns of spatial accessibility of the area's charter schools, within a 20-minute commuting time, to students aged 5–13 years by employing the enhanced two-step floating catchment area method using a Gaussian function. The next part of the study tests t",14.0, 08d18a146b365c661f0cb9c2821b72b8e1ba79ea,10.3390/su17052275,Cooling Efficiency of Urban Green Spaces Across Functional Zones: Mitigating Heat Island Effects Through Spatial Configuration,2025.0,Sustainability,Ying Wang; Yin Ren,"With the acceleration of urbanization, the urban heat island effect has garnered increasing attention. However, few studies have explored the differential impacts of urban green spaces on the UHI across various urban functional zones (UFZs). This study takes Xiamen Island as the research object and selects nine representative landscape pattern indices to characterize the spatial patterns of UGS in each urban functional zone. Through Pearson correlation analysis, four landscape indices—largest patch index (LPI), mean patch area (AREA_MN), area-weighted average shape index (SHAPE_AM), and aggregation index (AI)—were chosen to reveal the varying influences of UGS spatial patterns on the UHI in different urban functional zones. These four landscape indices reflect aspects such as area, shape complexity, density size, and variation, as well as the aggregation of UGS. To address the spatial autocorrelation of variables, a spatial regression model was established. Given that the parameters of the spatial lag model outperformed those of the spatial error model, the spatial lag model was selected. Key findings reveal that the cooling efficiency of UGS varies across UFZs. In urban residentia",14.0,https://doi.org/10.3390/su17052275 925c622a442843b5728f3f2681fd59c61780e5d6,10.1155/2022/9590704,House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis,2022.0,Wireless Communications and Mobile Computing,Ningyan Chen,"Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers, and prediction of house price trend as well as business decisions made by enterprises. By virtue of machine learning and statistical theory, the house price in Zhaoqing City from 2010 to 2020 will be researched, and the house price prediction model of Zhaoqing City will be constructed in this paper with several variables including GDP, proportion of tertiary industry, income of urban residents, fiscal revenue, land price, investment volume in real estate development, permanent resident population, population density, and proportion of urban population in net migration. First of all, the metho",14.0,https://downloads.hindawi.com/journals/wcmc/2022/9590704.pdf b85028275b011deb999436d9eb9aa1a2d46dcd7a,10.1108/ijhma-06-2020-0076,The capitalization of spatial healthcare accessibility into house prices in Taiwan: an application of spatial quantile regression,2020.0,International Journal of Housing Markets and Analysis,Ti-Ching Peng,"PurposePopulation ageing is fast becoming a major social concern across the globe. This ageing trend unavoidably fuels elders’ demand for healthcare services. As the main users of health care service, whether the healthcare is geographically approachable in local areas is more imperative to senior residents with restricted mobility. This paper proposes to examine the effect of elders’ healthcare accessibility on property prices of Taipei Metropolis, Taiwan.Design/methodology/approachLuo and Qi’s (2009) enhanced two-step floating catchment area method – taking both healthcare demand and supply into account – was used to measure three types of healthcare services: “physician-to-elder ratio”, “hospital bed-to-elder ratio” and “ambulance-to-elder ratio”. Spatial quantile regression (SQR) model was then used to examine the spatial effect of healthcare accessibility on different property price ranges.FindingsThe “physician-to-elder ratio” and “hospital bed-to-elder ratio” demonstrated expected consistent positive effects across all quantiles of property prices (p< 0.01) in SQR, and its effects aggravated as the quantiles of property prices rose. The “ambulance-to-elder ratio” demonstrate",14.0, 45b684ad227b7a2400c9bc9d5ed3253f40cd3792,10.1038/s41467-024-46548-6,Policy and market forces delay real estate price declines on the US coast,2024.0,Nature Communications,D. McNamara; Martin D. Smith; Z. Williams; S. Gopalakrishnan; C. E. Landry,"Despite increasing risks from sea-level rise (SLR) and storms, US coastal communities continue to attract relatively high-income residents, and coastal property values continue to rise. To understand this seeming paradox and explore policy responses, we develop the Coastal Home Ownership Model (C-HOM) and analyze the long-term evolution of coastal real estate markets. C-HOM incorporates changing physical attributes of the coast, economic values of these attributes, and dynamic risks associated with storms and flooding. Resident owners, renters, and non-resident investors jointly determine coastal property values and the policy choices that influence the physical evolution of the coast. In the coupled system, we find that subsidies for coastal management, such as beach nourishment, tax advantages for high-income property owners, and stable or increasing property values outside the coastal zone all dampen the effects of SLR on coastal property values. The effects, however, are temporary and only delay precipitous declines as total inundation approaches. By removing subsidies, prices would more accurately reflect risks from SLR but also trigger more coastal gentrification, as relative",14.0,https://www.nature.com/articles/s41467-024-46548-6.pdf be994437546b1ac879573045da725bd456fab585,10.3390/su17062627,Examining Residents’ Perceptions and Usage Preferences of Urban Public Green Spaces Through the Lens of Environmental Justice,2025.0,Sustainability,Yusheng Yang; Shuoning Tang,"Improving the equity of urban public green space is crucial for residents’ well-being and is a key objective in green space planning. While most existing studies focus primarily on the spatial distribution characteristics of green space resources, fewer explore the mechanisms influencing residents’ subjective perceptions and preferences. This study, based on a survey of 1419 residents in Hunan Province, constructs a structural equation model (SEM) to investigate the impact of the unequal distribution of urban public green space on residents’ happiness, mediated by social–psychological factors such as environmental perception, sense of security, and neighborhood cohesion. Additionally, a random forest (RF) algorithm is employed to identify the main factors influencing residents’ green space usage preferences. The results demonstrate that equity in green space distribution significantly influences residents’ happiness through environmental perception (path coefficient γ = 0.744, p = 0.001), security (γ = 0.664, p = 0.001), and neighborhood cohesion (γ = 0.830, p = 0.001). Key factors influencing residents’ preferences for green space use include age, housing prices, and walkability, ",14.0,https://doi.org/10.3390/su17062627 2e0ce9f8ce1fae3bc6247c345c7674910dc944b9,10.5755/j01.ee.29.2.20000,Panel Data Analysis of Public and Private Debt and House Price Influence on GDP in the European Union Countries,2018.0,,Vytautas Snieška; Daiva Burkšaitienė,"The panel data analysis of the influence of change in real public debt, real private debt, and deflated house prices on the GDP in selected European countries is performed. Least squares and autoregressive AR(p) model was used with cross-section and period both fixed by dummy variables. The research has confirmed strong negative influence of public debt with zero, one and two year’s lags as an independent variable on the GDP as the dependent variable. This is not surprising having in mind limited functionality of the European central bank as a lender of the last resort for the countries of the monetary union, that is, for the most of the analysed countries. This finding also confirms the necessity of the transformation of the European Monetary Union to the European Financial Union. Private debt has definite positive influence on the GDP as the dependent variable. It was confirmed by measuring this influence with zero, one and two and three years lags, but this positive influence was 2-3 times lower than the negative influence of public debt on the GDP. House prices unlagged have similar absolute value of positive influence on the GDP coefficient as the absolute value of the negativ",14.0,https://inzeko.ktu.lt/index.php/EE/article/download/20000/9368 fb48439d69079ff1fe892ad35dfa0570752f43d2,10.1016/j.pmedr.2017.06.012,Local environment and social factors in primary school children's afterschool commute in China,2017.0,Preventive medicine reports,J. Zacharias; Bai Zhen; Xi-Li Han; Yunshi Huang,"The rapid decline in young children's active commutes to and from school has prompted investigations into ways to raise activity levels. The period after school is recognized as very important in the daily activity regime of primary school children. In this study, we examine the relative effects of local environmental factors and socio-economic status on children's after-school commute mode choice. Environmental factors are pedestrian priority streets, street intersection density, motorways, shops, and play spaces. Property values are used as a proxy for income. Twenty-four school districts are selected using intersection density and motorway length as criteria. All children's exit behaviors were film-recorded on October weekdays and extracted as four choices–alone, in a group of children, on foot with a parent or guardian, on e-bike driven by an adult. A multinomial logistic regression reveals that gated communities, higher priced housing, motorways and bus stops are associated with children accompanied by adults. The presence of pedestrian streets is associated with children travelling alone and in groups. Greater travel distance is also associated with parents accompanying child",14.0,https://www.sciencedirect.com/science/article/pii/S2211335517301122/pdf 2f4f09d2d40cc45ef397d8d6c4cf0e43f77decf0,10.1109/hpcc/smartcity/dss.2018.00219,A Smart City Application: Business Location Estimator Using Machine Learning Techniques,2018.0,2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS),Tuğçe Bilen; Müge Erel-Özçevı̇k; Y. Yaslan; S. Oktug,"In smart cities, the business location is an important component of the complex decision process for the entrepreneurs. If a business such as shopping center, restau-rant, coffee shop, hospital, clothing etc. is opened in optimal location; not only the satisfaction level of the customers are enhanced with increased life quality and convenience, but also it maximizes the profit of the entrepreneurs. However, it is complex and long-term decision to find optimal location for a business. Therefore; we propose a smart business application that uses machine learning techniques to estimate the location of a business. The proposed system collects key feature values for a specific business and learns a prediction model for future data. It estimates features and suggests clusters of districts that have optimal locations for that specific business. The proposed system is evaluated on a use case that estimates the best possible locations of a restaurant according to main characteristics taken as inputs from entrepreneurs via a Web based application. These characteristics are Meal Price per customer, HouseHold Type, Gender, and Age. The first phase of decision process is estimating mentioned fe",14.0, ef703a0fbbc55cacaca23e2734e14cd273961269,10.3390/su15010841,"Restorative Effects of Park Visiting on Physiology, Psychology, and Society and the Factors Influencing Park Visiting",2023.0,Sustainability,Yang-Yang Gong; Zulpiya Mamat; Lei Shi; Feng-Lin Liu,"Park visits are beneficial for people’s physical and psychological health, as well as for the development of social relationships. This study investigated the degree of recovery of physical, psychological, and social aspects of residents in different types of parks and the influence of socio-demographic factors, personal factors, residential space attributes, and park characteristics on park visitation. The results show that tourists visiting urban parks have higher physiological and social recovery than those visiting suburban parks and that there are significant differences. Physical exercise, rest and relaxation, and spending time with family and children were the three most prevalent factors influencing park visits, while time constraints were the most important reason why residents failed to use parks. Socio demographics, residential spatial attributes, individual variables, and park characteristics explained 13.6%, 16.7%, 4.6%, and 2.9% of the total variance in park visit frequency, respectively. Residential green space, age, children under age seven, time spent in residential green space, willingness to spend time in nature, greenery, maintenance, and amenities were positive",14.0,https://www.mdpi.com/2071-1050/15/1/841/pdf?version=1672735006 d54b1dbba22dbbb09a4306e8dc745302486a98c9,10.7758/rsf.2017.3.3.05,Why Did People Move During the Great Recession?: The Role of Economics in Migration Decisions,2017.0,RSF,Brian L. Levy; Ted Mouw; Anthony Daniel Perez,"Labor migration offers an important mechanism to reallocate workers when there are regional differences in employment conditions. Whereas conventional wisdom suggests migration rates should increase during recessions as workers move out of areas that are hit hardest, initial evidence suggested that overall migration rates declined during the Great Recession, despite large regional differences in unemployment and growth rates. In this paper we use data from the American Community Survey to analyze internal migration trends before and during the economic downturn. First, we find only a modest decline in the odds of adults leaving distressed labor market areas during the Great Recession, which may result in part from challenges related to the housing price crash. Second, we estimate conditional logit models of destination choice for individuals who migrate across labor market areas; we find a substantial effect of economic factors such as labor demand, unemployment, and housing values. We also estimate latent class conditional logit models that test whether there is heterogeneity in preferences for destination characteristics among migrants. Over all, the latent class models suggest t",14.0,https://doi.org/10.7758/rsf.2017.3.3.05 3fec95611a8224038adc7e2e613e77c4a2bf3507,10.15847/dinamiacet-iul.wp.2020.06,Macroeconomic Determinants of Households' Indebtedness in Portugal: What Really Matters in the Era of Financialisation?,2022.0,International Journal of Finance & Economics,Ana Romão; Ricardo Barradas,"The objective of this paper is to perform a time series econometric analysis in order to empirically assess the macroeconomic determinants and the corresponding drivers of the Portuguese households’ indebtedness in the period 1988 to 2016. During that period, the Portuguese economy experienced a process of financialisation that contributed to an increase in Portuguese households’ indebtedness to unprecedented levels. The Portuguese households’ indebtedness played a crucial role in the recent sovereign debt crisis. Based on the existing literature, we hypothesize that Portuguese households’ indebtedness was due to seven macroeconomic determinants, notably housing prices, financial asset prices, the degree of personal income inequality, households’ labour income, the importance of welfare state expenditures, the fraction of the working-age population and the level of interest rates. Our findings reveal that financial asset prices, the degree of personal income inequality, households’ labour income and the fraction of the working-age population positively impact Portuguese households’ indebtedness, whereas the housing prices negatively impact Portuguese households’ indebtedness. Our f",14.0,https://repositorio.iscte-iul.pt/bitstream/10071/21961/1/WP_2020-06.pdf e9b54acd33342b9c859b2a289244ccd63af2f505,10.3390/su12104331,Varying Effects of Urban Tree Canopies on Residential Property Values across Neighborhoods,2020.0,Sustainability,Youngme Seo,"As more land area than ever is covered with impermeable surfaces causing problems in the environment, urban trees are effective not only in mitigating environmental problems in the built environment and reducing buildings’ energy use, but also in increasing social and economic benefits. However, the benefits urban trees provide are not evenly distributed but rather disproportionately distributed in high-income neighborhoods. The purpose of this study is to estimate the varying effects of urban trees based on a variety of factors that have influence on tree canopy coverage, including land constraints, new developments, financial capacity to maintain trees, and neighborhood characteristics. Using a unique dataset that includes 24,203 single-family residential sales from 2007 to 2015 merged with Urban Tree Canopy (UTC), this study utilizes spatial models to empirically evaluate the impact of UTC on residential property values in the housing market. Multi-Level Mixed (MLM) models are used to capture the varying effects of tree cover, based on land constraints, new development, financial capacity, and neighborhood characteristics. The findings show the effect of trees is positive and va",14.0,https://www.mdpi.com/2071-1050/12/10/4331/pdf?version=1590573041 2f9e29e92ef7a22b0331b559cb7f24d9283e81cd,10.1080/13658816.2018.1547386,Embedding road networks and travel time into distance metrics for urban modelling,2018.0,International Journal of Geographical Information Science,H. Crosby; Theodore Damoulas; S. Jarvis,"ABSTRACT Urban environments are restricted by various physical, regulatory and customary barriers such as buildings, one-way systems and pedestrian crossings. These features create challenges for predictive modelling in urban space, as most proximity-based models rely on Euclidean (straight line) distance metrics which, given restrictions within the urban landscape, do not fully capture spatial urban processes. Here, we argue that road distance and travel time provide effective alternatives, and we develop a new low-dimensional Euclidean distance metric based on these distances using an isomap approach. The purpose of this is to produce a valid covariance matrix for Kriging. Our primary methodological contribution is the derivation of two symmetric dissimilarity matrices ( and ), with which it is possible to compute low-dimensional Euclidean metrics for the production of a positive definite covariance matrix with commonly utilised kernels. This new method is implemented into a Kriging predictor to estimate house prices on 3,669 properties in Coventry, UK. We find that a metric estimating a combination of road distance and travel time, in both and , produces a superior house price p",14.0,https://www.tandfonline.com/doi/pdf/10.1080/13658816.2018.1547386?needAccess=true bf12ae3aa1856e3174c52b6ad528f8d219a252b8,10.1177/03611981231189738,Catchment-Area Delineation Approach Considering Travel Purposes for Station-Level Ridership Prediction Task,2023.0,Transportation Research Record,Chen Ma; Yanqiu Cheng; Shuang Zhang; Kuanmin Chen; Jie Wei; Xian-Biao Hu,"Station-catchment-area delineation is a key component of direct ridership models for urban rail-transport systems as it can determine the relationship between the urban-rail-transit station-level ridership and the variables within the station catchment area. The neglect of differences in the passenger-flow distribution for different travel purposes in previous studies has led to low accuracy of the obtained walk-to-station distances. Therefore, this paper proposes a station-catchment-area delineation method which is based on web map data to obtain accurate walk-to-station distances and considers differences in the distance thresholds and the ridership attraction intensity (RAI) for six travel purposes (corresponding to commercial, medical, residential, educational, administrative, and recreational land uses). In the case study, the ridership data of Xi’an Metro, the 2015 Xi’an Residential Travel Survey data, and the corresponding Gaode Map data are employed to extract passengers’ walking-distance distribution for several travel purposes to delineate the station catchment areas and build direct ridership models. Several geographically weighted regression (GWR) models are constructed",14.0, bd917fbc268f603e65254282c54b6f02bea23e74,10.56578/ataiml010103,House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection,2022.0,Acadlore Transactions on AI and Machine Learning,F. M. Basysyar; Gifthera Dwilestari,"In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.",14.0,https://library.acadlore.com/ATAIML/2022/1/1/ATAIML_01.01_03.pdf 5d5769c46815fd0be66684d0ddb1fcb27394ac66,10.1007/s10901-024-10124-7,Housing prices and points of interest in three Polish cities,2024.0,Journal of Housing and the Built Environment,R. Cellmer; M. Bełej; R. Trojanek,"This paper shows the usefulness of open source points-of-interest (POI) data for understanding the causes of volatility in residential property prices. Points of interest are unique locations or features cartographically mapped in space and explicitly connected to various aspects of human life. This point number, and density, primarily reflect a city’s spatial structure. They could therefore indicate a higher quality of life in a given urban zone, resulting in higher demand for housing in the area and, consequently, higher housing prices. This study was conducted in three Polish cities: Warsaw, Poznań and Olsztyn. Our research also attempted to establish which POI categories constitute stimulants and which are destimulants in the housing market. To determine the significance of POI in the price formation process, we used the quantile regression model and its spatial version, apart from classical regression models. The results of our research show the importance of POIs for the formation of housing prices. Individual POI categories are of minor importance, while the simultaneous presence of many different POIs definitely has a positive impact on housing prices.",14.0,https://doi.org/10.1007/s10901-024-10124-7 a3d845d54ac202b15ee42f9be3a871f4c8de52ef,10.3390/ijgi9040259,Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation,2020.0,ISPRS Int. J. Geo Inf.,R. Suárez-Vega; Juan M. Hernández,"Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.",14.0,https://www.mdpi.com/2220-9964/9/4/259/pdf?version=1587895139 59b2e5f8dd55163cf0dfe3bff3d366750f345b54,10.1016/j.resourpol.2019.101523,Revisiting the link between resource windfalls and subnational crowding out for local mining economies in Chile,2019.0,,M. Oyarzo; Dusan Paredes,"Abstract Literature on the resource curse argues that resource windfalls, such as those resulting from a commodity price boom, crowd out several determinants of long-term fiscal income (Papyrakis and Gerlagh, 2006). Although empirical literature tests this theory at an intercountry context, similar attention has not been paid to that of subnational governments. This different type of spatial scope would reveal how low-tier governments strategically behave in regard to resource windfalls and covering local costs. Any strategic behavior will directly impact the local community's well-being due to the role played by subnational governments in providing local public goods. We contribute to this gap in the literature by analyzing how the resource windfalls from mining taxes in Chile crowd out local collected revenue such as a residential and a commercial property tax. Using panel data for 345 Chilean municipalities between 2008 and 2017, we pursue the causal effect derived from a subnational crowding out hypothesis, measured as the cross substitution between an additional monetary unit received from windfalls. We also take advantage of the exogenous allocation rule for the distribution ",14.0, 4add1d0476f9b79f965d23c8e65ab5c961140823,10.1920/wp.cem.2013.0214,Instrumental variables estimation of a generalized correlated random coefficients model,2013.0,,Matthew A. Masten; Alexander Torgovitsky,"We study identi?cation and estimation of the average treatment effect in a correlated random coefficients model that allows for ?rst stage heterogeneity and binary instruments. The model also allows for multiple endogenous variables and interactions between endogenous variables and covariates. Our identi?cation approach is based on averaging the coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. This identi?cation strategy suggests a transparent and computationally straightforward estimator of a trimmed average treatment effect constructed as the average of kernel-weighted linear regres-sions. We develop this estimator and establish its vn–consistency and asymptotic normality. Monte Carlo simulations show excellent ?nite-sample performance that is comparable in precision to the standard two-stage least squares estimator. We apply our results to analyze the effect of air pollution on house prices, and ?nd substantial heterogeneity in ?rst stage instrument effects as well as heterogeneity in treatment effects that is consistent with household sorting.",14.0,https://arxiv.org/pdf/1310.6643 0e3ac967c407c31a730846c08adb56ac5403d0e5,10.3846/ijspm.2022.15962,REAL ESTATE VALUATION MODELS PERFORMANCE IN PRICE PREDICTION,2022.0,International Journal of Strategic Property Management,Adela Deaconu; Anuța Buiga; Helga Tothăzan,"Using a sample of 900 apartments from Cluj-Napoca, Romania, containing selling transactions for the second semester of 2019, and data for 33 locational, physical and neighbourhood-related attributes (socio-cultural, environmental, and urbanism related), our research objective is to test the performance in price prediction, and hence the utility, of the Artificial Neural Networking (ANN), as artificial intelligence model versus the Generalized Linear Model (GLM), as a regression model. By contributing to an ongoing debate, our empirical findings confirm the results of a predominant group of earlier studies, namely the superiority of ANN. Precisely, we found that ANN can better predict selling prices and provides stability of results. Additionally, we addressed the critiques related to the transparency of results, showing that ANN also has the ability to illustrate the significance of the different attributes of real estate, if appropriate statistical indicators are used. These findings can serve the different real estate valuation purposes, including that of the review of valuation reports.",14.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/15962/10893 ebb13f62ba15566e18bc5fca69c3e2df2036ab5d,10.3389/fpsyg.2022.903276,A Cross-Temporal Meta-Analysis on Marital Satisfaction of Chinese Couples,2022.0,Frontiers in Psychology,Fengzhan Li; Chen Chen; Jin-Run Wang; Hai-Yun Peng; Lin Wu; L. Ren; Lei Song; Yin-chuan Jin; Qun Yang,"Objective To investigate the changing trend of Chinese couples' marital satisfaction and its relationship with social changes. Methods A cross-temporal meta-analysis was performed on 118 original studies (n = 31,909) reporting marital satisfaction of Chinese couples from 1994 to 2020, primarily using correlation analysis and regression analysis. Results (1) Overall, the marital satisfaction of Chinese couples showed a downward trend over time. (2) Men's marital satisfaction displayed almost no change, while women's marital satisfaction had a more obvious downward trend. (3) Changes in macrosocial factors (per capita consumption expenditure, housing prices, old-age dependency ratio, and divorce rate) could significantly predict the downward trend of marital satisfaction, especially for women. Conclusion In the past 27 years, the overall marital satisfaction level of Chinese couples has shown a downward trend, and there are gendered differences, which may be related to changes in the socioeconomic and cultural environments.",14.0,https://www.frontiersin.org/articles/10.3389/fpsyg.2022.903276/pdf e84e671c37fcb94d9ca8f42c22a957fa5c5316ee,10.2139/ssrn.3483966,The Economics of Mass Shootings,2019.0,Social Science Research Network,Abel Brodeur; Hasin Yousaf,"We perform an empirical investigation of the socioeconomic determinants and consequences of all mass shootings in the U.S. from 2000 to 2015. We first manually search for and collect information on perpetrators. We find that approximately 40% (45%) of shooters were in financial distress (unemployed or out of the labor force) at the moment of the shooting, suggesting that economic distress may trigger rise in shooting. We then investigate the economic consequences of mass shootings. In order to obtain the causal effects of shootings, we exploit the inherent randomness in the success or failure of mass shootings. We find that, on average, successful mass shooting have economically significant negative effects on targeted counties' employment and earnings. As well, successful mass shootings decrease housing prices and consumer confidence and increase absenteeism. Last, we employ an instrumental variable strategy and show that national media coverage of mass shootings exacerbate their local economic consequences.",14.0,https://doi.org/10.2139/ssrn.3483966 e606bc2674353b4df95f3a5fb334d7306f82ec82,10.1016/j.jtrangeo.2018.03.004,"The impact of fixed links on population development, housing and the labour market: The case of Norway",2018.0,,S. Andersen; M. D. Gutiérrez; Ø. Nilsen; Trude Tørset,"Abstract This study compares 11 fixed link projects in Norway, linking islands to the mainland. The fixed links substantially increase the accessibility between the islands and the mainland. Thus, interaction between the islands and the mainland increases. Large infrastructure improvements may not only affect the transport patterns but also the population and companies in the connected areas. We apply a difference-in-difference method to analyse the impacts from the increased accessibility. The findings indicate growth in housing prices, population, commuting and employment on the islands. The growth rates of these variables depend on the case characteristics. Urban cases appear to promote larger growth on the island. The contribution of this paper to the literature is that while there have been several studies that have involved single projects, a comparison of several cases that combines assessments of housing and labour markets is still rare in the literature.",14.0,http://hdl.handle.net/11250/2600204 2a007a36c9d040ea166693e371e708b19008dbe4,10.1007/s10663-024-09611-5,"Drivers of European housing prices in the new millennium: demand, financial, and supply determinants",2024.0,Empirica,Aleš Melecký; D. Pakši,"Many countries in Europe have experienced a steady increase in housing prices over the past decade, which continued even during the recent crisis. We analyze a panel of 15 European countries over the period 2000–2020. We find that demand-side determinants, such as GDP, unemployment, wage and population, strongly influence housing prices. Nevertheless, we suggest that construction costs, access to finance (credit to GDP), and financing costs (long-term interest rate) should be included to avoid biased results. We find that financial development can significantly affect housing prices in the long run. We confirm the robustness of our results by conducting a lag sensitivity analysis of selected determinants. In addition, we find a negative effect of the GFC and a positive effect of the Covid crisis on housing prices. Furthermore, we find that countries with a mild reaction to or a quick recovery from the GFC experienced significantly higher housing price growth.",14.0,https://link.springer.com/content/pdf/10.1007/s10663-024-09611-5.pdf 4844a7fdfe6b4999f825bc84747f042e22fec426,10.1080/00036846.2022.2110212,Misalignments in house prices and economic growth in Europe,2022.0,Applied Economics,J. C. Cuestas; M. Kukk; Natalia Levenko,"ABSTRACT In this paper we investigate house price misalignments and how they affect the real economy. We estimate the long-term relationship between house prices and the fundamentals that determine long-term house prices for a panel of European countries with dynamic OLS, using data from 2005–2018. We find that income has been the main driver of fundamental house prices in all countries, while the supply of dwellings has calmed the rise in house prices in some of them. We calculate house price misalignments, which are deviations of house prices from the fundamental value, and we employ them in the growth model. The results of the growth regression indicate that house price imbalances amplify business cycles in the short term, but in the long term house price overvaluations slow economic growth down. The findings imply that it is crucial to take measures to stabilize housing cycles.",14.0,https://repositori.uji.es/xmlui/bitstream/10234/200117/1/81888.pdf ed3d14b8ea2fcb1435e1cf83c8e332e216d59377,10.1108/ijhma-03-2020-0020,The effects of uncertainty shocks on global housing markets,2020.0,,Mei-Se Chien; Nur Setyowati," Purpose This paper aims to investigate how different uncertainty shocks affect international housing prices. Design/methodology/approach The authors set up a model of housing price instability with four uncertainty variables and apply the panel generalized method of moments method and quantile regression to estimate the linear and non-linear linkages among the variables based on data of 56 countries from 2001Q1 to 2018Q2. Findings Some empirical findings are as follows. Higher macroeconomic uncertainty and global economic policy risk increase housing price instability, whereas greater financial uncertainty and geopolitical risk present reverse effect. Four uncertainty variables are good signals for housing price changes in Asia, and geopolitical risk takes leading role in Europe. Macroeconomic uncertainty positively impacts housing price instability only at a low or middle level in all regions, as financial uncertainty, global economic policy uncertainty and geopolitical risk effects in all regions are smaller at the middle or high level of housing price instability; this confirms the existence of non-linear correlation between each variable. Originality/value The findings h",14.0, e19a1a83c723854406a227f7725dd49818d57f23,10.1016/j.healthplace.2019.102172,Associations of neighbourhood sociodemographic characteristics with depressive and anxiety symptoms in older age: Results from a 5-wave study over 15 years.,2019.0,Health and Place,I. Motoc; E. Timmermans; D. J. Deeg; B. Penninx; M. Huisman,"We examined the long-term association between objective neighbourhood sociodemographic characteristics (index of socioeconomic position (SEP), average income, percent low-income earners, average house price, percent immigrants and urban density) with depressive and anxiety symptoms, covering five 3-year waves of the Longitudinal Aging Study Amsterdam (n = 3,772). Multi-level regression models assessed each neighbourhood-level characteristic separately, adjusting for individual-level covariates. A higher percentage of immigrants and higher urban density, but not other neighbourhood characteristics, were significantly associated with depressive and anxiety symptoms over time in models adjusted for individual SEP. Results of time interaction models indicated that the associations were stable over the 15-year period.",14.0,https://www.sciencedirect.com/science/article/pii/S1353829218310852/pdf cceb4fcfd35c0d1c8063ac93306ace3de6b393f9,10.5089/9781484378243.001,House Price Synchronization and Financial Openness: A Dynamic Factor Model Approach,2018.0,Social Science Research Network,Mitsuru Katagiri,"This paper investigates the developments in house price synchronization across countries by a dynamic factor model using a country- and city-level dataset, and examines what drives the synchronization. The empirical results indicate that: (i) the degree of synchronization has been rising since the 1970s, and (ii) a large heterogeneity in the degree of synchronization exists across countries and cities. A panel and cross-sectional regression analysis show that the heterogeneity of synchronization is partly accounted for by the progress in financial and trade openness. Also, the city-level analysis implies that the international synchronization is mainly driven by the city-level connectivity between large and international cities.",14.0,https://www.elibrary.imf.org/doc/IMF001/25462-9781484378243/25462-9781484378243/Other_formats/Source_PDF/25462-9781484379578.pdf 6e53356cfdf50d23acc9e53d536267ad8ed9f841,10.1093/jeg/lbad007,"Natural disasters, risk and migration: evidence from the 2017 Pohang earthquake in Korea",2023.0,Journal of Economic Geography,Hyejin Kim; Jongkwan Lee," Using the 2017 Pohang earthquake in South Korea as a natural experiment, we examined the responses of the population. By constructing a counterfactual of Pohang using synthetic controls, we found that the earthquake significantly decreased the local population. To investigate the mechanisms of population decrease, we gathered administrative data on the universe of address changes and self-reported reasons for migration. While out-migration due to changes in the residential environment increased significantly, job-related migration showed little change. This out-migration also led to a depreciation in rental housing prices. Our results suggest that local residents perceived the earthquake as a negative amenity.",14.0,https://academic.oup.com/joeg/advance-article-pdf/doi/10.1093/jeg/lbad007/50172160/lbad007.pdf efe49b1b0fc09953fc3aff653968d44133a006f2,10.1016/j.eneco.2021.105733,Coal-to-gas fuel switching and its effects on housing prices,2021.0,Energy Economics,Nathaly M. Rivera; S. Loveridge,We derive causal property value impacts of the coal-to-gas fuel switching conversion implemented by several power plants in the United States. We use an extensive dataset of property transactions around the country and adopt several spatial difference-in-difference approaches that use records of residential property transactions of homes with wind exposure and proximity to the switching plants before and after the switch. A triple-differences control function estimator using coal-fired plants that did not innovate strengthens these estimations. Our results indicate that the shutdown of coal-fired generators increases property values of downwind homes by 15% in the immediate vicinity of fuel-switching plants (,14.0,https://doi.org/10.1016/j.eneco.2021.105733 601cb6323343e28e70e6be5e0bd640374ea3a496,10.1080/09538259.2022.2149923,Determinants of Residential Investment Growth Rate in the US Economy (1992–2019),2022.0,Review of Political Economy,Gabriel Petrini; Lucas Teixeira,"ABSTRACT The leading role of residential investment in the business cycles is a robust stylized fact for the US economy. The housing bubble of the 2000s has increased interest in the macroeconomic relevance of this expenditure. However, there is still controversy surrounding its determinants. This article is an attempt to fulfill this gap. We propose to combine mortgage interest rate and house price inflation — two of the most relevant variables according to the literature — in a single index: houses’ own-rate of interest. This index represents the real cost of buying houses, so we expect a negative relationship with the growth rate of residential investment. Regarding the US economy from 1992 to 2019, we find a unidirectional negative correlation between houses’ own-rate of interest and residential investment growth rate in the long run. In the short-run adjustment process, we report no statistically significant effect of residential investment growth rate on houses’ own- rate of interest. Our results are robust to lag order specification and show that houses own-rate of interest explains more than half of the variability of residential investment rate of growth.",14.0, 3a5a84cf4f348596fd03c2b1eca8c0fe7b5f178d,10.1177/10780874211028814,Residential Location and Household Spending: Exploring the Relationship Between Neighborhood Characteristics and Transportation and Housing Costs,2021.0,Urban Affairs Review,Andrew Schouten,"Using data from the Panel Study of Income Dynamics and a seven-category neighborhood typology, this analysis examines the relationship between urban form and household spending. Results suggest that poor households living in urban areas have lower transportation expenditures than their counterparts in sprawling suburbs. Lower transportation costs, however, do not offset high housing prices, with poor households paying particularly high premiums for housing in the densest, most transit-rich neighborhoods. Households above the poverty threshold also benefit from reductions in transportation costs, especially in intensely urban areas. Nevertheless, these low transportation costs are not associated with lower overall expenditures; instead, they countervail high housing premiums, meaning that the most transit-rich neighborhoods do not offer cost savings relative to other neighborhood types. Findings highlight the need to expand the supply of both transit and housing in communities where poor households can leverage affordable transportation options to reduce their combined expenditure burden.",14.0, 3a8bf4e59b7736be836fb492841da1ddaef35549,10.53479/29472,A house price-at-risk model to monitor the downside risk for the Spanish housing market,2023.0,Documento de trabajo,G. Gánics; María Rodríguez-Moreno,"We present a house price-at-risk (HaR) model that fits the historical developments in the Spanish housing market. By means of quantile regressions we show that a model including quarterly house price growth, a misalignment measure and a consumer confidence index is able to accurately forecast the developments in the Spanish housing market up to two years ahead. We also show how the HaR model can be used to monitor the downside risk.",14.0,https://repositorio.bde.es/bitstream/123456789/29472/1/dt2244e.pdf a221cbb983800ef86a32fffe6a9b793fd1a1cef1,10.1109/icoei51242.2021.9452820,Prediction of House Price Using Machine Learning Algorithms,2021.0,2021 5th International Conference on Trends in Electronics and Informatics (ICOEI),G. Kumar; D. M. Rani; Neeraja Koppula; S. Ashraf,"People are very careful when they want to buy a new house with market strategies and their budgets. The objective of this paper is to predict the house prices for non-house holders based on their aspirations and financial provisions. By analyzing different parameters like area of the house, square feet of the house, no of floors in the house etc. This research work has utilized the dataset from Kaggle.. An analysis is performed by applying advanced machine learning regression techniques such as Linear regression, KNN Regression, Random Forest Regression, Decision Tree Regression, Extra Trees Regression etc. to attain the most efficient and least error driven regression technique. From the analysis performed, an observation has been made that Catboost Regression Algorithm has outperformed other algorithms. The model predicts the final output with respect to correlated attributes in the dataset.",14.0,