{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T13:42:57Z","timestamp":1784209377189,"version":"3.55.0"},"reference-count":119,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,14]],"date-time":"2023-05-14T00:00:00Z","timestamp":1684022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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 and tailored methods, which could guide further research.<\/jats:p>","DOI":"10.3390\/ijgi12050200","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T08:28:16Z","timestamp":1684139296000},"page":"200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Survey of Methods and Input Data Types for House Price Prediction"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9592-1843","authenticated-orcid":false,"given":"Margot","family":"Geerts","sequence":"first","affiliation":[{"name":"Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seppe","family":"vanden Broucke","sequence":"additional","affiliation":[{"name":"Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium"},{"name":"Department of Business Informatics and Operations Management, UGent, Tweekerkenstraat 2, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-0504","authenticated-orcid":false,"given":"Jochen","family":"De Weerdt","sequence":"additional","affiliation":[{"name":"Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1086\/260169","article-title":"Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition","volume":"82","author":"Rosen","year":"1974","journal-title":"J. Political Econ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/0166-0462(92)90039-4","article-title":"Specification and estimation of hedonic housing price models","volume":"22","author":"Can","year":"1992","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104919","DOI":"10.1016\/j.landusepol.2020.104919","article-title":"Understanding house price appreciation using multi-source big geo-data and machine learning","volume":"111","author":"Kang","year":"2021","journal-title":"Land Use Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"58","DOI":"10.14716\/ijtech.v10i1.975","article-title":"A Comparison of Bandwidth and Kernel Function Selection in Geographically Weighted Regression for House Valuation","volume":"10","author":"Yacim","year":"2019","journal-title":"Int. J. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106409","DOI":"10.1016\/j.landusepol.2022.106409","article-title":"Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia","volume":"123","author":"Gao","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106167","DOI":"10.1016\/j.landusepol.2022.106167","article-title":"Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis","volume":"119","author":"Sisman","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, Y., Liu, J., Xu, S., and Zhao, Y. (2016). An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5010004"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100777","DOI":"10.1016\/j.mex.2019.100777","article-title":"Method for conducting systematic literature review and meta-analysis for environmental science research","volume":"7","author":"Mengist","year":"2020","journal-title":"MethodsX"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S19","DOI":"10.1016\/j.cities.2012.06.006","article-title":"Spatial econometrics, land values and sustainability: Trends in real estate valuation research","volume":"29","author":"Krause","year":"2012","journal-title":"Cities"},{"key":"ref_11","first-page":"312","article-title":"Specifying the effect of location in multivariate valuation models for residential properties: A critical evaluation from the mass appraisal perspective","volume":"25","author":"Mccluskey","year":"2007","journal-title":"Prop. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1108\/14635780310483656","article-title":"Real estate appraisal: A review of valuation methods","volume":"21","author":"Pagourtzi","year":"2003","journal-title":"J. Prop. Investig. Financ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, D., and Li, V.J. (2019). Mass appraisal models of real estate in the 21st century: A systematic literature review. Sustainability, 11.","DOI":"10.3390\/su11247006"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"180","DOI":"10.3991\/ijoe.v14i03.8420","article-title":"Artificial Neural Networks and the Mass Appraisal of Real Estate","volume":"14","author":"Zhou","year":"2018","journal-title":"Int. J. Online Eng. (IJOE)"},{"key":"ref_15","unstructured":"Geerts, M., De Weerdt, J., and vanden Broucke, S. (2022). A Survey of Methods and Input Data Types for House Price Prediction: Literature List. KU Leuven RDR, V2."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1556\/032.2016.66.3.8","article-title":"Valuation methods for the housing market: Evidence from Budapest","volume":"66","author":"Kutasi","year":"2016","journal-title":"Acta Oecon"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104889","DOI":"10.1016\/j.landusepol.2020.104889","article-title":"A mass appraisal assessment study using machine learning based on multiple regression and random forest","volume":"99","author":"Yilmazer","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"05021003","DOI":"10.1061\/(ASCE)UP.1943-5444.0000651","article-title":"Spatial Autoregressive Analysis and Modeling of Housing Prices in City of Toronto","volume":"147","author":"Zhang","year":"2021","journal-title":"J. Urban Plan. Dev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1007\/s10618-021-00789-x","article-title":"Boosting house price predictions using geo-spatial network embedding","volume":"35","author":"Das","year":"2021","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_20","unstructured":"Bengio, Y., Goodfellow, I., and Courville, A. (2017). Deep Learning, MIT Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s10109-017-0257-y","article-title":"Housing price prediction: Parametric versus semi-parametric spatial hedonic models","volume":"20","author":"Montero","year":"2018","journal-title":"J. Geogr. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1111\/j.1467-9787.2011.00713.x","article-title":"A Spatial and Temporal Autoregressive Local Estimation for the Paris Housing Market","volume":"51","author":"Maury","year":"2011","journal-title":"J. Reg. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"04014047","DOI":"10.1061\/(ASCE)UP.1943-5444.0000270","article-title":"Heterogeneity in Spatial Correlation and Influential Factors on Property Prices of Submarkets Categorized by Urban Dwelling Spaces","volume":"142","author":"Hui","year":"2016","journal-title":"J. Urban Plan. Dev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jhe.2011.11.001","article-title":"Hedonic house prices and spatial quantile regression","volume":"21","author":"Liao","year":"2012","journal-title":"J. Hous. Econ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jasi\u0144ska, E., and Preweda, E. (2021). Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Krak\u00f3w. Sustainability, 13.","DOI":"10.3390\/su13169339"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, C., Ye, X., Ren, F., Wan, Y., Ning, P., and Du, Q. (2016). Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0164553"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xue, C., Ju, Y., Li, S., Zhou, Q., and Liu, Q. (2020). Research on accurate house price analysis by using gis technology and transport accessibility: A case study of xi\u2019an, china. Symmetry, 12.","DOI":"10.3390\/sym12081329"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s12145-021-00589-3","article-title":"Learning with self-attention for rental market spatial dynamics in the Atlanta metropolitan area","volume":"14","author":"Zhou","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/095999196368899","article-title":"Hedonic modelling, housing submarkets and residential valuation","volume":"13","author":"Adair","year":"1996","journal-title":"J. Prop. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"217","DOI":"10.2457\/srs.33.217","article-title":"Predicting Housing Prices in Central Ankara, Turkey Based on Spatial Dependence Analysis","volume":"33","author":"Gultekin","year":"2002","journal-title":"Stud. Reg. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1068\/b2780","article-title":"Valuing Locational Externalities: A GIS and Multilevel Modelling Approach","volume":"29","author":"Orford","year":"2002","journal-title":"Environ. Plan. B Plan Des."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3141\/2115-16","article-title":"Effects of Transportation Accessibility on Residential Property Values","volume":"2115","author":"Viegas","year":"2009","journal-title":"Transp. Res. Rec. J. Transp. Res. Board."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s12076-009-0030-z","article-title":"Predicting housing prices at alternative locations and under alternative scenarios of the spatial job distribution","volume":"2","author":"Osland","year":"2009","journal-title":"Lett. Spat. Resour. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1108\/17538271111153022","article-title":"The impact of proximity to cell phone towers on residential property values","volume":"4","author":"Filippova","year":"2011","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1080\/09654313.2012.673569","article-title":"Spatial Determinants of Housing Price Values in Istanbul","volume":"20","author":"Koramaz","year":"2012","journal-title":"Eur. Plan. Stud."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10037-013-0074-9","article-title":"Hybrid multilevel STAR models for hedonic house prices","volume":"33","author":"Brunauer","year":"2013","journal-title":"Jahrb Reg."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1177\/1471082X13475385","article-title":"Modelling house prices using multilevel structured additive regression","volume":"13","author":"Brunauer","year":"2013","journal-title":"Stat. Model."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.landurbplan.2013.08.009","article-title":"Classification and valuation of urban green spaces\u2014A hedonic house price valuation","volume":"120","author":"Panduro","year":"2013","journal-title":"Landsc. Urban Plan."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/21606544.2014.951399","article-title":"Comparing the impact of road noise on property prices in two separated markets","volume":"4","author":"Franck","year":"2015","journal-title":"J. Environ. Econ. Policy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1108\/JERER-03-2016-0014","article-title":"Modelling the impact of earthquake activity on real estate values: A multi-level approach","volume":"10","author":"Keskin","year":"2017","journal-title":"J. Eur. Real Estate Res."},{"key":"ref_41","first-page":"57","article-title":"Does urban centrality influence residential prices? An analysis for the Barcelona Metropolitan Area","volume":"16","year":"2017","journal-title":"Rev. Constr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1111\/roiw.12303","article-title":"Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines","volume":"64","author":"Hill","year":"2018","journal-title":"Rev. Income Wealth"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10479-020-03556-1","article-title":"Developing automated valuation models for estimating property values: A comparison of global and locally weighted approaches","volume":"306","author":"Doumpos","year":"2021","journal-title":"Ann. Oper. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1111\/rsp3.12382","article-title":"House price valuation of environmental amenities: An application of GIS-derived data","volume":"14","author":"Osland","year":"2020","journal-title":"Reg. Sci. Policy Pract."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1080\/00420989550012492","article-title":"Spatial Estimation of Housing Prices and Locational Rents","volume":"32","year":"1995","journal-title":"Urban Stud."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s11146-007-9036-8","article-title":"Spatial dependence, housing submarkets, and house price prediction","volume":"35","author":"Bourassa","year":"2007","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1080\/10835547.2007.12091188","article-title":"Prediction of housing location price by a multivariate spatial method: Cokriging","volume":"29","year":"2007","journal-title":"J. Real Estate Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s10109-009-0090-z","article-title":"Area-to-point Kriging in spatial hedonic pricing models","volume":"11","author":"Yoo","year":"2009","journal-title":"J. Geogr. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1080\/02723638.2013.778662","article-title":"A Coregionalized Model to Predict Housing Prices","volume":"34","year":"2013","journal-title":"Urban Geogr."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1080\/10293523.2013.11082563","article-title":"An online real estate valuation model for control risk taking: A spatial approach","volume":"42","author":"Larraz","year":"2013","journal-title":"Investig. Anal. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.trd.2015.02.011","article-title":"The effect of road traffic noise on the prices of residential property\u2014A case study of the polish city of Olsztyn","volume":"36","author":"Senetra","year":"2015","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10640-016-0076-5","article-title":"Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets","volume":"69","author":"Filatova","year":"2018","journal-title":"Environ. Resour. Econ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chica-Olmo, J., Cano-Guervos, R., and Chica-Rivas, M. (2019). Estimation of Housing Price Variations Using Spatio-Temporal Data. Sustainability, 11.","DOI":"10.3390\/su11061551"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1111\/geoj.12289","article-title":"Determination of buffer zone for negative externalities: Effect on housing prices","volume":"185","year":"2019","journal-title":"Geogr. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"417","DOI":"10.2307\/3146899","article-title":"Out of Sight, Out of Mind? Using GIS to Incorporate Visibility in Hedonic Property Value Models","volume":"78","author":"Paterson","year":"2002","journal-title":"Land Econ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1080\/00420980220135545","article-title":"Estimating Neighbourhood Effects in House Prices: Towards a New Hedonic Model Approach","volume":"39","author":"Tse","year":"2002","journal-title":"Urban Stud."},{"key":"ref_57","first-page":"25","article-title":"Modelling interactions of location with specific value of housing attributes","volume":"21","author":"Villeneuve","year":"2003","journal-title":"Prop. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1111\/j.1467-9787.2008.00569.x","article-title":"Spatial hedonic models of airport noise, proximity, and housing prices","volume":"48","author":"Cohen","year":"2008","journal-title":"J. Reg. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11146-007-9053-7","article-title":"Determinants of House Prices: A Quantile Regression Approach","volume":"37","author":"Zietz","year":"2008","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1007\/s11146-009-9209-8","article-title":"The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices","volume":"42","author":"Zhu","year":"2011","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1080\/02673037.2012.725832","article-title":"Applying Directed Acyclic Graphs to Assist Specification of a Hedonic Model","volume":"27","author":"Cho","year":"2012","journal-title":"Hous. Stud."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s11146-011-9359-3","article-title":"Spatial and Temporal Dependence in House Price Prediction","volume":"47","author":"Liu","year":"2013","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_63","first-page":"116","article-title":"Housing market analysis using a hierarchical\u2013spatial approach: The case of Belo Horizonte, Minas Gerais, Brazil","volume":"1","year":"2014","journal-title":"Reg. Stud. Reg. Sci."},{"key":"ref_64","first-page":"343","article-title":"Valuation of environmental pollution in the city of Madrid: An application with hedonic models and spatial quantile regression","volume":"1","author":"Chasco","year":"2015","journal-title":"Rev. D\u00e9conomie Reg. Urbaine"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1111\/grow.12147","article-title":"Proximity to Natural Amenities: A Seemingly Unrelated Hedonic Regression Model with Spatial Durbin and Spatial Error Processes","volume":"47","author":"Hand","year":"2016","journal-title":"Growth Chang."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.jtrangeo.2016.06.016","article-title":"Long-term impact of network access to bike facilities and public transit stations on housing sales prices in Portland, Oregon","volume":"54","author":"Welch","year":"2016","journal-title":"J. Transp. Geogr."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.spasta.2017.07.010","article-title":"MCMC Bayesian spatial filtering for hedonic models in real estate markets","volume":"22","author":"Gargallo","year":"2017","journal-title":"Spat. Stat."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1080\/00401706.2017.1317290","article-title":"Hierarchical Spatially Varying Coefficient Process Model","volume":"59","author":"Kim","year":"2017","journal-title":"Technometrics"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"6454","DOI":"10.1002\/2016WR019606","article-title":"The impact of water quality in Narragansett Bay on housing prices","volume":"53","author":"Liu","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3368\/le.93.1.127","article-title":"Valuing Public Goods, the Time to Capitalization, and Network Externalities: A Spatial Hedonic Regression Analysis","volume":"93","author":"Ohler","year":"2017","journal-title":"Land Econ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/09599916.2017.1400575","article-title":"House price determinants in Athens: A spatial econometric approach","volume":"34","author":"Stamou","year":"2017","journal-title":"J. Prop. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1111\/gean.12136","article-title":"Bayesian Spatial Filtering for Hedonic Models: An Application for the Real Estate Market","volume":"50","author":"Gargallo","year":"2018","journal-title":"Geogr. Anal."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1007\/s11146-017-9610-7","article-title":"Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data","volume":"57","author":"Simlai","year":"2018","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.trd.2018.04.001","article-title":"Walking accessibility and property prices","volume":"62","author":"Yang","year":"2018","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/09599916.2018.1562490","article-title":"Analysis of spatial variance clustering in the hedonic modeling of housing prices","volume":"36","year":"2019","journal-title":"J. Prop. Res."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Wang, W.C., Chang, Y.J., and Wang, H.C. (2019). An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8060249"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1177\/0042098019879382","article-title":"A novel hedonic price modelling approach for estimating the impact of transportation infrastructure on property prices","volume":"58","author":"Lieske","year":"2021","journal-title":"Urban Stud."},{"key":"ref_78","first-page":"987","article-title":"Exploring a multilevel approach with spatial effects to model housing price in San Jos\u00e9, Costa Rica","volume":"3","year":"2021","journal-title":"Environ. Plan B Urban Anal. City Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1108\/13664381211211046","article-title":"Spatial variation as a determinant of house price","volume":"17","author":"McCord","year":"2012","journal-title":"J. Financ. Manag. Prop. Constr."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1108\/IJHMA-09-2012-0043","article-title":"Understanding rental prices in the UK: A comparative application of spatial modelling approaches","volume":"7","author":"McCord","year":"2014","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1111\/pirs.12003","article-title":"A Bayesian approach to hedonic price analysis","volume":"93","author":"Wheeler","year":"2014","journal-title":"Pap. Reg. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2015.12.002","article-title":"Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches","volume":"57","author":"Helbich","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Liu, J., Yang, Y., Xu, S., Zhao, Y., Wang, Y., and Zhang, F. (2016). A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation. Entropy, 18.","DOI":"10.3390\/e18080303"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1007\/s10994-017-5639-3","article-title":"Varying-coefficient models for geospatial transfer learning","volume":"106","author":"Bussas","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1080\/13658816.2016.1263731","article-title":"Geographically weighted regression with parameter-specific distance metrics","volume":"31","author":"Lu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.apgeog.2016.12.012","article-title":"The economic value of streets: Mix-scale spatio-functional interaction and housing price patterns","volume":"79","author":"Shen","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Chen, X., Li, Q., Yu, X., Chen, J., and Guo, J. (2017). Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110358"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Lan, F., Wu, Q., Zhou, T., and Da, H. (2018). Spatial Effects of Public Service Facilities Accessibility on Housing Prices: A Case Study of Xi\u2019an, China. Sustainability, 10.","DOI":"10.3390\/su10124503"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Hu, L., Chun, Y., and Griffith, D.A. (2019). A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110508"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1108\/IJHMA-09-2019-0097","article-title":"House price estimation using an eigenvector spatial filtering approach","volume":"13","author":"McCord","year":"2019","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11146-019-09723-x","article-title":"Combining Property Price Predictions from Repeat Sales and Spatially Enhanced Hedonic Regressions","volume":"61","author":"Oust","year":"2020","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"102387","DOI":"10.1016\/j.trd.2020.102387","article-title":"Accessibility to transit, by transit, and property prices: Spatially varying relationships","volume":"85","author":"Yang","year":"2020","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"100470","DOI":"10.1016\/j.spasta.2020.100470","article-title":"Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction","volume":"41","author":"Dambon","year":"2021","journal-title":"Spat. Stat."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1023\/B:REAL.0000011153.04496.42","article-title":"The Hierarchical Trend Model for Property Valuation and Local Price Indices","volume":"28","author":"Francke","year":"2004","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10109-007-0054-0","article-title":"Forecasting prices of single family homes using GIS-defined neighborhoods","volume":"10","author":"Kaboudan","year":"2008","journal-title":"J. Geogr. Syst."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s11146-010-9234-7","article-title":"The Time-Series Properties of House Prices: A Case Study of the Southern California Market","volume":"44","author":"Gupta","year":"2012","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s00168-015-0660-6","article-title":"Exploring, modelling and predicting spatiotemporal variations in house prices","volume":"54","author":"Fotheringham","year":"2015","journal-title":"Ann. Reg. Sci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1108\/14635780610642971","article-title":"Architecture for a real estate analysis information system using GIS techniques integrated with fuzzy theory","volume":"24","author":"Pagourtzi","year":"2006","journal-title":"J. Prop. Investig. Financ."},{"key":"ref_99","first-page":"169","article-title":"Realistic uncertainty estimation of the market value based on a Fuzzy-Bayesian sales comparison approach","volume":"141","author":"Alkhatib","year":"2015","journal-title":"ZFV-Geodasie Geoinf. Landmanag."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"221","DOI":"10.53383\/100242","article-title":"A Localized Model for Residential Property Valuation: Nearest Neighbor with Attribute Differences","volume":"20","author":"Cheung","year":"2017","journal-title":"Int. Real Estate Rev."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1108\/IJHMA-04-2021-0041","article-title":"Distance in geographic and characteristics space for real estate pricing","volume":"15","author":"Ozhegov","year":"2021","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neucom.2013.07.035","article-title":"Semiparametric spatial effects kernel minimum squared error model for predicting housing sales prices","volume":"124","author":"Shim","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"s626","DOI":"10.1016\/S1003-6326(12)61652-5","article-title":"Real estate appraisal system based on GIS and BP neural network","volume":"21","author":"Liu","year":"2011","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"121067","DOI":"10.1016\/j.techfore.2021.121067","article-title":"The Spatial neural network model with disruptive technology for property appraisal in real estate industry","volume":"173","author":"Lin","year":"2021","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"114590","DOI":"10.1016\/j.eswa.2021.114590","article-title":"Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain","volume":"171","year":"2021","journal-title":"Expert. Syst. Appl."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1007\/s10614-020-09973-5","article-title":"A New Appraisal Model of Second-Hand Housing Prices in China\u2019s First-Tier Cities Based on Machine Learning Algorithms","volume":"57","author":"Xu","year":"2021","journal-title":"Comput. Econ."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1111\/j.1538-4632.1972.tb00458.x","article-title":"Generating Models by the Expansion Method: Applications to Geographical Research","volume":"4","author":"Casetti","year":"1972","journal-title":"Geogr. Anal."},{"key":"ref_108","unstructured":"Kaggle (2023, April 21). House Sales in King County, USA. Available online: https:\/\/www.kaggle.com\/datasets\/harlfoxem\/housesalesprediction."},{"key":"ref_109","unstructured":"Kaggle (2022, January 25). Melbourne Housing Market. Available online: https:\/\/www.kaggle.com\/anthonypino\/melbourne-housing-market."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"De Cock, D. (2011). Ames, Iowa: Alternative to the boston housing data as an end of semester regression project. J. Stat. Education., 19.","DOI":"10.1080\/10691898.2011.11889627"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/0095-0696(78)90006-2","article-title":"Hedonic housing prices and the demand for clean air","volume":"5","author":"Harrison","year":"1978","journal-title":"J. Environ. Econ. Manag."},{"key":"ref_112","unstructured":"Ade-Ojo, J. (2022, February 01). Predicting House Prices with Machine Learning. Available online: https:\/\/towardsdatascience.com\/predicting-house-prices-with-machine-learning-62d5bcd0d68f."},{"key":"ref_113","unstructured":"Bershadskiy, I. (2022, February 01). Using Machine Learning Algorithm for Predicting House Valuations. Available online: https:\/\/yalantis.com\/blog\/predictive-algorithm-for-house-price\/."},{"key":"ref_114","unstructured":"Chow, C. (2022, February 01). Machine Learning for Property Valuation. Available online: https:\/\/chrischow.github.io\/dataandstuff\/2019-09-15-machine-learning-for-property-valuation\/."},{"key":"ref_115","unstructured":"Cuturi, M.P., and Etchebarne, G. (2022, February 01). Real Estate Pricing with Machine Learning & Non-Traditional Data Sources. Available online: https:\/\/tryolabs.com\/blog\/2021\/06\/25\/real-estate-pricing-with-machine-learning\u2013non-traditional-data-sources."},{"key":"ref_116","unstructured":"Zillow (2022, February 01). What Is a Zestimate? Zillow\u2019s Zestimate Accuracy. Available online: https:\/\/www.zillow.com\/z\/zestimate\/."},{"key":"ref_117","unstructured":"Ahmed, E., and Moustafa, M. (2016, January 9\u201311). House price estimation from visual and textual features. Proceedings of the 8th International Joint Conference on Computational Intelligence, Porto, Portugal."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Piao, Y., Chen, A., and Shang, Z. (2019, January 2\u20135). Housing Price Prediction Based on CNN. Proceedings of the 2019 9th International Conference on Information Science and Technology (ICIST), Hulunbuir, China.","DOI":"10.1109\/ICIST.2019.8836731"},{"key":"ref_119","unstructured":"Xiong, S., Sun, Q., and Zhou, A. (2020). Internet of Vehicles. Technologies and Services Toward Smart Cities. IOV 2019. Lecture Notes in Computer Science, Springer."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/5\/200\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:41Z","timestamp":1760124881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/5\/200"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,14]]},"references-count":119,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["ijgi12050200"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12050200","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,14]]}}}