{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T22:17:16Z","timestamp":1784153836955,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"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>With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often not interesting for mortgage lenders with assets spread out all over the country. That is why, with the current abundance of national geospatial datasets, this paper implements and compares three hedonic pricing models (linear regression, geographically weighted regression, and extreme gradient boosting\u2014XGBoost) to model real estate appraisals values for five large municipalities in different parts of the Netherlands. The appraisal values used to train the model are provided by Stater N.V., which is the largest mortgage service provider in the Netherlands. Out of the three implemented models, the XGBoost model has the highest accuracy. XGBoost can explain 83% of the variance with an RMSE of \u20ac65,312, an MAE of \u20ac43,625, and an MAPE of 6.35% across the five municipalities. The two most important variables in the model are the total living area and taxation value, which were taken from publicly available datasets. Furthermore, a comparison is made between indexation and XGBoost, which shows that the XGBoost model is able to more accurately predict the appraisal values of different types of houses. The remaining unexplained variance is most probably caused by the lack of good indicators for the condition of the house. Overall, this paper highlights the benefits of open geospatial datasets to build a national real estate appraisal model.<\/jats:p>","DOI":"10.3390\/ijgi11020125","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T21:22:15Z","timestamp":1644441735000},"page":"125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Spatial Determinants of Real Estate Appraisals in The Netherlands: A Machine Learning Approach"],"prefix":"10.3390","volume":"11","author":[{"given":"Evert","family":"Guliker","sequence":"first","affiliation":[{"name":"Faculty of Behavioural, Management and Social Sciences (BMS), University of Twente, 7522 NB Enschede, The Netherlands"},{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"},{"name":"Stater N.V., 3826 PA Amersfoort, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7845-1763","authenticated-orcid":false,"given":"Erwin","family":"Folmer","sequence":"additional","affiliation":[{"name":"Faculty of Behavioural, Management and Social Sciences (BMS), University of Twente, 7522 NB Enschede, The Netherlands"},{"name":"Kadaster, 7311 KZ Apeldoorn, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marten","family":"van Sinderen","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"ref_1","unstructured":"AFM (2021, February 02). Hypotheek in Relatie tot Waarde Huis (LTV). Available online: https:\/\/afm.nl\/nl-nl\/consumenten\/themas\/producten\/hypotheek\/hoeveel-lenen\/maximale-hypotheek."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1080\/13504851.2018.1430319","article-title":"The macroeconomic effects of the LTV and LTI ratios in Ireland","volume":"25","author":"Lozej","year":"2018","journal-title":"Appl. Econ. Lett."},{"key":"ref_3","unstructured":"De Nederlandsche Bank (2019). The Quality and Integrity of Residential Real Estate Appraisals, De Nederlandsche Bank. Occasional Studies."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1016\/j.jenvman.2011.06.019","article-title":"The value of urban open space: Meta-analyses of contingent valuation and hedonic pricing results","volume":"92","author":"Brander","year":"2011","journal-title":"J. Environ. Manag."},{"key":"ref_5","unstructured":"Potrawa, T. (2020). Hedonic Pricing Model for Rotterdam Housing Market. [Master\u2019s Thesis, TU Delft]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1080\/09654313.2017.1376314","article-title":"Hedonic pricing analysis of the influence of urban green spaces onto residential prices: The case of Leipzig, Germany","volume":"26","author":"Liebelt","year":"2018","journal-title":"Eur. Plan. Stud."},{"key":"ref_7","first-page":"173","article-title":"A big data\u2013based geographically weighted regression model for public housing prices: A case study in Singapore","volume":"109","author":"Cao","year":"2019","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_8","unstructured":"Waarderingskamer (2021, March 01). Hoe de WOZ-Waarde tot Stand Komt. Available online: https:\/\/waarderingskamer.nl\/klopt-mijn-woz-waarde\/totstandkoming-woz-waarde\/."},{"key":"ref_9","unstructured":"Kadaster (2021, January 15). BAG, Addresses and Buildings Key Register. Available online: https:\/\/kadaster.nl\/zakelijk\/registraties\/basisregistraties\/bag."},{"key":"ref_10","unstructured":"Calcasa (2021, March 02). WOX-Waarde. Available online: https:\/\/calcasa.nl\/wox-online."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s11146-007-9068-0","article-title":"Developing a house price index for the Netherlands: A practical application of weighted repeat sales","volume":"37","author":"Jansen","year":"2008","journal-title":"J. Real Estate Financ. Econ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jue.2006.07.007","article-title":"Depreciation of housing capital, maintenance, and house price inflation: Estimates from a repeat sales model","volume":"61","author":"Harding","year":"2007","journal-title":"J. Urban Econ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/0094-1190(78)90004-9","article-title":"Hedonic prices, price indices and housing markets","volume":"5","author":"Goodman","year":"1978","journal-title":"J. Urban Econ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0169-2046(00)00039-6","article-title":"The value of trees, water and open space as reflected by house prices in the Netherlands","volume":"48","author":"Luttik","year":"2000","journal-title":"Landsc. Urban Plan."},{"key":"ref_15","first-page":"405","article-title":"A comparison of localized regression models in a hedonic house price context","volume":"29","author":"Farber","year":"2006","journal-title":"Can. J. Reg. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1007\/s00500-018-3090-4","article-title":"Affordable levels of house prices using fuzzy linear regression analysis: The case of Shanghai","volume":"22","author":"Zhou","year":"2018","journal-title":"Soft Comput."},{"key":"ref_17","unstructured":"CBS (2021, January 16). Prijzen Koopwoningen. Available online: https:\/\/cbs.nl\/nl-nl\/reeksen\/prijzen-koopwoningen."},{"key":"ref_18","unstructured":"Fotheringham, A., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons."},{"key":"ref_19","first-page":"1","article-title":"The Spatial Dimension of House Prices","volume":"4","author":"Gong","year":"2017","journal-title":"A+ BE|Archit. Built Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0166-0462(02)00028-5","article-title":"The return of centralization to Chicago: Using repeat sales to identify changes in house price distance gradients","volume":"33","author":"McMillen","year":"2003","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tomal, M. (2020). Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060346"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"251","DOI":"10.3846\/1648-715X.2008.12.251-269","article-title":"Modelling the impact of wind farms on house prices in the UK","volume":"12","author":"Sims","year":"2008","journal-title":"Int. J. Strateg. Prop. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/13658816.2018.1545158","article-title":"Multiscale geographically and temporally weighted regression: Exploring the spatiotemporal determinants of housing prices","volume":"33","author":"Wu","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, L., and Lu, F. (2019). Exploring housing rent by mixed geographically weighted regression: A Case study in Nanjing. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8100431"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1111\/gean.12259","article-title":"Short-Term Rental Platform in the Urban Tourism Context: A Geographically Weighted Regression (GWR) and a Multiscale GWR (MGWR) Approaches","volume":"53","author":"Shabrina","year":"2021","journal-title":"Geogr. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, H., Qiu, R., and Chen, W. (2020). Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction. Smart Service Systems, Operations Management, and Analytics, Springer International Publishing.","DOI":"10.1007\/978-3-030-30967-1"},{"key":"ref_28","first-page":"2151","article-title":"Prediction of House Price Using XGBoost Regression Algorithm","volume":"12","author":"Avanijaa","year":"2021","journal-title":"Turk. J. Comput. Math. Educ. (TURCOMAT)"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1108\/14635789710163793","article-title":"Estimating the influence of transport on house prices: Evidence from Hong Kong","volume":"15","author":"So","year":"1997","journal-title":"J. Prop. Valuat. Invest."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"419","DOI":"10.3844\/jmssp.2012.419.434","article-title":"Performance of multiple linear regression and nonlinear neural networks and fuzzy logic techniques in modelling house prices","volume":"8","author":"Amri","year":"2012","journal-title":"J. Math. Stat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compenvurbsys.2005.07.009","article-title":"Visualising space and time in crime patterns: A comparison of methods","volume":"31","author":"Brunsdon","year":"2007","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.regsciurbeco.2012.07.002","article-title":"Price and transaction volume in the Dutch housing market","volume":"43","author":"Englund","year":"2013","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wheeler, D.C., and P\u00e1ez, A. (2010). Geographically Weighted Regression. Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Springer.","DOI":"10.1007\/978-3-642-03647-7_22"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/S0095-0696(02)00048-7","article-title":"Hazardous waste sites and housing appreciation rates","volume":"45","author":"McCluskey","year":"2003","journal-title":"J. Environ. Econ. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1111\/jmcb.12011","article-title":"House Prices and Fundamentals: 355 Years of Evidence","volume":"45","author":"Ambrose","year":"2012","journal-title":"J. Money Credit. Bank."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.eneco.2014.12.012","article-title":"Does energy efficiency matter to home-buyers? An investigation of EPC ratings and transaction prices in England","volume":"48","author":"Fuerst","year":"2015","journal-title":"Energy Econ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1080\/00343401003601925","article-title":"The impact of industrial sites on residential property values: A hedonic pricing analysis from the Netherlands","volume":"45","year":"2011","journal-title":"Reg. Stud."},{"key":"ref_38","unstructured":"Kadaster\/ESRI Nederland (2021, March 05). DKK: Land Lot Dataset. Available online: https:\/\/arcgis.com\/home\/group.html?id=eb452ccc59e0431c8b42b06c7e7a6fee#overview."},{"key":"ref_39","unstructured":"CBS (2021, March 01). 100 \u00d7 100 Squaremeter Statistics. Available online: https:\/\/cbs.nl\/nl-nl\/dossier\/nederland-regionaal\/geografische-data\/kaart-van-100-meter-bij-100-meter-met-statistieken."},{"key":"ref_40","unstructured":"RVO (2021, March 01). Energy Labels Dataset. Available online: https:\/\/www.ep-online.nl\/."},{"key":"ref_41","first-page":"1247","article-title":"Multiscale Geographically Weighted Regression (MGWR)","volume":"107","author":"Fotheringham","year":"2017","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_42","unstructured":"Kadaster (2021, February 15). Vastgoed Dashboard, Prijsindex. Available online: https:\/\/kadaster.nl\/zakelijk\/vastgoedinformatie\/vastgoedcijfers\/vastgoeddashboard\/prijsindex."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/2\/125\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:17:04Z","timestamp":1760134624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/2\/125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["ijgi11020125"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11020125","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,9]]}}}