IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v49y2022i6p1715-1740.html
   My bibliography  Save this article

Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression

Author

Listed:
  • Yigong Hu
  • Binbin Lu
  • Yong Ge
  • Guanpeng Dong

Abstract

Spatial heterogeneity is important for exploring data relationships between real estate price and its influential factors. The geographically weighted regression (GWR) technique has been frequently adopted for this purpose. In this study, we collected a second-hand real estate house price data set of Wuhan, in which each property is located the same as the community it belongs to. Thus, this data set possesses a typical characteristic, that is, dozens or even hundreds of observations could be allocated to one pair of coordinates, but vary in their attributes. This specific feature might lead to serious problems with bandwidth optimisations and coefficient estimates for calibrating the GWR model. We then proposed an extension by combining the hierarchical linear model (HLM) and GWR, namely HLM-GWR to cope with these problems. Results show that the HLM-GWR performs much better than the conventional GWR and HLM technique in terms of bandwidth optimisation, coefficient estimates. With a controlled simulation test, we again validated the advantage of the HLM-GWR model in comparison to both the HLM and GWR in handling this specific scenario. Overall, HLM-GWR is workable and should be recommended in this case or other scenarios with observations of similar spatial distributions.

Suggested Citation

  • Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    • Handle: RePEc:sae:envirb:v:49:y:2022:i:6:p:1715-1740
    DOI: 10.1177/23998083211063885
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/23998083211063885
    Download Restriction: no
    File URL: https://libkey.io/10.1177/23998083211063885?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    2. Goodman, Allen C. & Thibodeau, Thomas G., 2003. "Housing market segmentation and hedonic prediction accuracy," Journal of Housing Economics, Elsevier, vol. 12(3), pages 181-201, September.
    3. Guanpeng Dong & Jing Ma & Richard Harris & Gwilym Pryce, 2016. "Spatial Random Slope Multilevel Modeling Using Multivariate Conditional Autoregressive Models: A Case Study of Subjective Travel Satisfaction in Beijing," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(1), pages 19-35, January.
    4. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    5. Dale Hattis & David E. Burmaster, 1994. "Assessment of Variability and Uncertainty Distributions for Practical Risk Analyses," Risk Analysis, John Wiley & Sons, vol. 14(5), pages 713-730, October.
    6. Goodman, Allen C., 1978. "Hedonic prices, price indices and housing markets," Journal of Urban Economics, Elsevier, vol. 5(4), pages 471-484, October.
    7. Bourassa, Steven C. & Hamelink, Foort & Hoesli, Martin & MacGregor, Bryan D., 1999. "Defining Housing Submarkets," Journal of Housing Economics, Elsevier, vol. 8(2), pages 160-183, June.
    8. Gelfand, Alan E. & Banerjee, Sudipto & Sirmans, C.F. & Tu, Yong & Eng Ong, Seow, 2007. "Multilevel modeling using spatial processes: Application to the Singapore housing market," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3567-3579, April.
    9. Lee Chun Chang & Hui-Yu Lin, 2012. "The Impact of Neighborhood Characteristics on Housing Prices-An Application of Hierarchical Linear Modeling," International Journal of Management and Sustainability, Conscientia Beam, vol. 1(2), pages 31-44.
    10. Liv Osland, 2010. "An Application of Spatial Econometrics in Relation to Hedonic House Price Modelling," Journal of Real Estate Research, American Real Estate Society, vol. 32(3), pages 289-320.
    11. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    12. A. Fotheringham & Ricardo Crespo & Jing Yao, 2015. "Exploring, modelling and predicting spatiotemporal variations in house prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 417-436, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sakamoto, Maiko & Ullah, S.M. Asik & Tani, Masakazu, 2024. "Impacts of refugee influx on the local economy and environmental degradation in Bangladesh: A spatial multilevel autoregressive analysis," World Development, Elsevier, vol. 183(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chris Leishman & Greg Costello & Steven Rowley & Craig Watkins, 2013. "The Predictive Performance of Multilevel Models of Housing Sub-markets: A Comparative Analysis," Urban Studies, Urban Studies Journal Limited, vol. 50(6), pages 1201-1220, May.
    2. Paul Harris & Bruno Lanfranco & Binbin Lu & Alexis Comber, 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    3. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    4. José-María Montero & Román Mínguez & Gema Fernández-Avilés, 2018. "Housing price prediction: parametric versus semi-parametric spatial hedonic models," Journal of Geographical Systems, Springer, vol. 20(1), pages 27-55, January.
    5. Kopczewska, Katarzyna & Ćwiakowski, Piotr, 2021. "Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants," Land Use Policy, Elsevier, vol. 103(C).
    6. Gjestland, Arnstein & McArthur, David Philip & Osland, Liv & Thorsen, Inge, 2014. "The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 136-151.
    7. Yaxiong Ma & Sucharita Gopal, 2018. "Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework," Sustainability, MDPI, vol. 10(4), pages 1-27, March.
    8. Chang-Lin Mei & Feng Chen & Wen-Tao Wang & Peng-Cheng Yang & Si-Lian Shen, 2021. "Efficient estimation of heteroscedastic mixed geographically weighted regression models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(1), pages 185-206, February.
    9. Luc Anselin & Pedro Amaral, 2024. "Endogenous spatial regimes," Journal of Geographical Systems, Springer, vol. 26(2), pages 209-234, April.
    10. Damian Przekop, 2022. "Artificial Neural Networks vs Spatial Regression Approach in Property Valuation," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 14(2), pages 199-223, June.
    11. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    12. Jos魍ar𨁍ontero-Lorenzo & Beatriz Larraz-Iribas, 2012. "Space-time approach to commercial property prices valuation," Applied Economics, Taylor & Francis Journals, vol. 44(28), pages 3705-3715, October.
    13. Keemin Sohn, 2013. "Feature Mapping the Seoul Metro Station Areas Based on a Self-Organizing Map," Journal of Urban Technology, Taylor & Francis Journals, vol. 20(4), pages 23-42, October.
    14. Hamidreza Rabiei-Dastjerdi & Gavin McArdle, 2021. "Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin," Land, MDPI, vol. 10(6), pages 1-16, May.
    15. David C. Wheeler & Antonio Páez & Jamie Spinney & Lance A. Waller, 2014. "A Bayesian approach to hedonic price analysis," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 663-683, August.
    16. Paloma Taltavull de La Paz, 2021. "Predicting housing prices. A long term housing price path for Spanish regions," LARES lares-2021-4dra, Latin American Real Estate Society (LARES).
    17. Liebelt, Veronika & Bartke, Stephan & Schwarz, Nina, 2018. "Revealing Preferences for Urban Green Spaces: A Scale-sensitive Hedonic Pricing Analysis for the City of Leipzig," Ecological Economics, Elsevier, vol. 146(C), pages 536-548.
    18. Tom Kauko, 2004. "A Comparative Perspective on Urban Spatial Housing Market Structure: Some More Evidence of Local Sub-markets Based on a Neural Network Classification of Amsterdam," Urban Studies, Urban Studies Journal Limited, vol. 41(13), pages 2555-2579, December.
    19. Mats Wilhelmsson & Jianyu Zhao, 2018. "Risk Assessment of Housing Market Segments: The Lender’s Perspective," JRFM, MDPI, vol. 11(4), pages 1-22, October.
    20. A. Fotheringham & Ricardo Crespo & Jing Yao, 2015. "Exploring, modelling and predicting spatiotemporal variations in house prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 417-436, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:49:y:2022:i:6:p:1715-1740. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.