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Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines

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  • Robert Hill
  • Michael Scholz

Abstract

Determining how and when to use geospatial data (i.e., longitudes and latitudes for each house) is probably the most pressing open question in the house price index literature. This issue is particularly timely for national statistical offices in the European Union who are now required by Eurostat to produce official house price indexes. Our solution combines the hedonic imputation method with a flexible hedonic model that captures geospatial data using a nonparametric spline surface. For Sydney, Australia, we find that the extra precision provided by geospatial data as compared with postcode dummies has only a marginal impact on the resulting hedonic index. This is good news for resource-stretched statistical offices. We nevertheless observe a slight downward bias when postcodes are used (which gets much larger when postcodes are replaced by bigger Residex regions). This bias can be attributed to a gradual shift of sold houses towards worse locations within each postcode (Residex region) during our sample period.

Suggested Citation

  • Robert Hill & Michael Scholz, 2016. "Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines," ERES eres2016_146, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2016_146
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    Cited by:

    1. Reusens, Peter & Vastmans, Frank & Damen, Sven, 2023. "A new framework to disentangle the impact of changes in dwelling characteristics on house price indices," Economic Modelling, Elsevier, vol. 123(C).
    2. Enwei Zhu & Jing Wu & Hongyu Liu & Xindian Li, 2022. "Within‐City Spatial Distribution, Heterogeneity and Diffusion of House Price: Evidence from a Spatiotemporal Index for Beijing," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(3), pages 621-655, September.
    3. Robert J. Hill & Alicia N. Rambaldi, 2022. "Hedonic Models and House Price Index Numbers," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 413-444, Springer.
    4. Lepinteur, Anthony & Waltl, Sofie R., 2020. "Tracking Owners' Sentiments: Subjective Home Values, Expectations and House Price Dynamics," Department of Economics Working Paper Series 299, WU Vienna University of Economics and Business.
    5. Robert J. Hill & Miriam Steurer & Sofie R. Waltl, 2019. "Owner-Occupied Housing, Inflation, and Monetary Policy," Graz Economics Papers 2019-05, University of Graz, Department of Economics.
    6. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2021. "Higher frequency hedonic property price indices: a state-space approach," Empirical Economics, Springer, vol. 61(1), pages 417-441, July.
    7. Hill, Robert J. & Trojanek, Radoslaw, 2022. "An evaluation of competing methods for constructing house price indexes: The case of Warsaw," Land Use Policy, Elsevier, vol. 120(C).
    8. Yohan Kim & Scott Kelly & Deepu Krishnan & Jay Falletta & Kerryn Wilmot, 2022. "Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    9. Robert J. Hill & Miriam Steurer & Sofie R. Waltl, 2017. "Owner Occupied Housing in the CPI and Its Impact On Monetary Policy During Housing Booms and Busts," Graz Economics Papers 2017-12, University of Graz, Department of Economics.
    10. R. H. Ilyasov & V. A. Plotnikov, 2022. "Oil Production and Carbon Emissions: Spline Analysis of Relationships," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 5.
    11. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2021. "Real estate listings and their usefulness for hedonic regressions," Empirical Economics, Springer, vol. 61(6), pages 3239-3269, December.
    12. Alexander Daminger, 2021. "Subsidies to Homeownership and Central City Rent," Working Papers 210, Bavarian Graduate Program in Economics (BGPE).
    13. Julian Granna & Wolfgang Brunauer & Stefan Lang, 2022. "Proposing a global model to manage the bias-variance tradeoff in the context of hedonic house price models," Working Papers 2022-12, Faculty of Economics and Statistics, Universität Innsbruck.
    14. W. Erwin Diewert & Chihiro Shimizu, 2022. "Residential Property Price Indexes: Spatial Coordinates Versus Neighborhood Dummy Variables," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(3), pages 770-796, September.
    15. Ahlfeldt, Gabriel M. & Heblich, Stephan & Seidel, Tobias, 2023. "Micro-geographic property price and rent indices," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    16. Jason R. Bailey & Davide Lauria & W. Brent Lindquist & Stefan Mittnik & Svetlozar T. Rachev, 2022. "Hedonic Models of Real Estate Prices: GAM and Environmental Factors," Papers 2210.14266, arXiv.org.
    17. Robert S. Martin, 2022. "Democratic Aggregation: Issues and Implications for Consumer Price Indexes," Economic Working Papers 600, Bureau of Labor Statistics.
    18. Koetter, Michael & Marek, Philipp & Mavropoulos, Antonios, 2021. "Real estate transaction taxes and credit supply," Discussion Papers 04/2021, Deutsche Bundesbank.
    19. Robert J. Hill & Norbert Pfeifer & Miriam Steurer, 2020. "The Airbnb Rent-Premium and the Crowding-Out of Long-Term Rentals," Graz Economics Papers 2020-06, University of Graz, Department of Economics.
    20. Daniel Melser & Robert J. Hill, 2019. "Residential Real Estate, Risk, Return and Diversification: Some Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 59(1), pages 111-146, July.

    More about this item

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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