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Categorical Variable Problem In Real Estate Submarket Determination With Gwr Model Cover

Categorical Variable Problem In Real Estate Submarket Determination With Gwr Model

Real Estate Management and Valuation
Volume 30 (2022): Issue 4 (December 2022)
By: Sebastian Gnat  
Open Access
|Dec 2022

References

  1. Assunção, R. M., Neves, M. C., Câmara, G., & da Costa Freitas, C. (2006). Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science, 20(7), 797–811. https://doi.org/10.1080/13658810600665111
    Search in Google ScholarBack to article
  2. Benjamin, J., Guttery, R., & Sirmans, C. (2004). Mass Appraisal: An Introduction to Multiple Regression Analysis for Real Estate Valuation. Journal of Real Estate Practice and Education, 7. https://doi.org/10.1080/10835547.2004.12091602
    Search in Google ScholarBack to article
  3. Beracha, E., Hardin III, W. G., & Skiba, H. M. (2018). Real Estate Market Segmentation: Hotels as Exemplar. JOURNAL OF REAL ESTATE FINANCE AND ECONOMICS, 56(2), 252–273. https://doi.org/10.1007/s11146-017-9598-z
    Search in Google ScholarBack to article
  4. Bourassa, S., Cantoni, E., & Hoesli, M. (2007). Spatial Dependence, Housing Submarkets, and House Price Prediction. The Journal of Real Estate Finance and Economics, 35, 143–160. https://doi.org/10.1007/s11146-007-9036-8
    Search in Google ScholarBack to article
  5. Bourassa, S., Cantoni, E., &Hoesli, M. (2010). Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods. Journal of Real Estate Research, 32, 139–160. https://doi.org/10.1080/10835547.2010.12091276
    Search in Google ScholarBack to article
  6. Bradbury, K. L., Mayer, C. J., & Case, K. E. (2001). Property tax limits, local fiscal behavior, and property values: evidence from Massachusetts under Proposition 212. Journal of Public Economics, 80(2), 287–311. https://doi.org/10.1016/S0047-2727(00)00081-5
    Search in Google ScholarBack to article
  7. Cellmer, R., Cichulska, A., & Belej, M. (2020). Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression. ISPRS International Journal of Geo-Information, 9, 380. https://doi.org/10.3390/ijgi9060380
    Search in Google ScholarBack to article
  8. Chen, C. F., & Rothschild, R. (2010). An application of hedonic pricing analysis to the case of hotel rooms in Taipei. Tourism Economics, 16(3), 685–694. https://doi.org/10.5367/000000010792278310
    Search in Google ScholarBack to article
  9. Chen, J., & Hao, Q. (2010). Submarket, Heterogeneity and Hedonic Prediction Accuracy of Real Estate Prices: Evidence from Shanghai. International Real Estate Review, 13, 190–217. https://doi.org/10.53383/100125
    Search in Google ScholarBack to article
  10. Cox, T., &Hurtubia, R. (2020). Subdividing the sprawl: Endogenous segmentation of housing submarkets in expansion areas of Santiago, Chile. Environment and Planning B: Urban Analytics and City Science, 48(7), 1770–1786. https://doi.org/10.1177/2399808320947728
    Search in Google ScholarBack to article
  11. Dmytrów, K., Gdakowicz, A., &Putek-Szeląg, E. (2020). Methods of Analyzing Qualitative Variable Correlation on the Real Estate Market. Real Estate Management and Valuation, 28(1), 80–90. https://doi.org/doi:10.2478/remav-2020-000710.2478/remav-2020-0007
    Search in Google ScholarBack to article
  12. Doszyń, M. (2020). Econometric Support of a Mass Valuation Process. Folia Oeconomica Stetinensia, 20(1), 81–94. https://doi.org/10.2478/foli-2020-0005
    Search in Google ScholarBack to article
  13. Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480
    Search in Google ScholarBack to article
  14. Fotheringham, S. A., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression. JOHN WILEY & SONS, LTD.
    Search in Google ScholarBack to article
  15. Gabrielli, L., Giuffrida, S., & Trovato, M. R. (2017). Gaps and Overlaps of Urban Housing Sub-market: Hard Clustering and Fuzzy Clustering Approaches. In S. Stanghellini, P. Morano, M. Bottero, & A. Oppio (Eds.), APPRAISAL: FROM THEORY TO PRACTICE (pp. 203–219). SPRINGER INTERNATIONAL PUBLISHING AG. https://doi.org/10.1007/978-3-319-49676-4_15
    Search in Google ScholarBack to article
  16. Helbich, M., Brunauer, W., Hagenauer, J., & Leitner, M. (2013). Data-Driven Regionalization of Housing Markets. Annals of the Association of American Geographers, 103(4), 871–889. https://doi.org/10.1080/00045608.2012.707587
    Search in Google ScholarBack to article
  17. Hwang, S. (2015). Residential Segregation, Housing Submarkets, and Spatial Analysis: St. Louis and Cincinnati as a Case Study. Housing Policy Debate, 25(1), 91–115. https://doi.org/10.1080/10511482.2014.934703
    Search in Google ScholarBack to article
  18. Keskin, B., & Watkins, C. (2016). Defining spatial housing submarkets: Exploring the case for expert delineated boundaries. Urban Studies, 54(6), 1446–1462. https://doi.org/10.1177/0042098015620351
    Search in Google ScholarBack to article
  19. Kestens, Y., Thériault, M., &Rosiers, F. (2006). Heterogeneity in hedonic modelling of house prices: Looking at buyers’ household profiles. Journal of Geographical Systems, 8, 61–96. https://doi.org/10.1007/s10109-005-0011-8
    Search in Google ScholarBack to article
  20. Kopczewska, K. (2020). Applied Spatial Statistics and Econometrics: Data Analysis in R. https://doi.org/10.4324/9781003033219
    Search in Google ScholarBack to article
  21. Kopczewska, K. (2021). Spatial machine learning: new opportunities for regional science. Annals of Regional Science. https://doi.org/10.1007/s00168-021-01101-x
    Search in Google ScholarBack to article
  22. Kopczewska, K., &Ćwiakowski, P. (2021). Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants. Land Use Policy, 103. https://doi.org/10.1016/j.landusepol.2021.105292
    Search in Google ScholarBack to article
  23. Korteweg, A., & Sorensen, M. (2016). Estimating Loan-to-Value Distributions. Real Estate Economics, 44(1), 41–86. https://doi.org/http://doi.org/10.1111/1540-6229.1208610.1111/1540-6229.12086
    Search in Google ScholarBack to article
  24. Manjula, R., Jain, S., Srivastava, S., & Rajiv Kher, P. (2017). Real estate value prediction using multivariate regression models. IOP Conference Series: Materials Science and Engineering, 263(4). https://doi.org/10.1088/1757-899X/263/4/042098
    Search in Google ScholarBack to article
  25. Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Stewart Fotheringham, A. (2019). MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6). https://doi.org/10.3390/ijgi8060269
    Search in Google ScholarBack to article
  26. Santos, J. A. C., Fernández-Gámez, M., Solano-Sánchez, M. Á., Rey-Carmona, F. J., del Rio, L., & Caridad, L. (2021). Valuation models for holiday rentals’ daily rates: Price composition based on booking.com. Sustainability (Switzerland), 13(1), 1–16. https://doi.org/10.3390/su13010292
    Search in Google ScholarBack to article
  27. Schnare, A. B., &Struyk, R. J. (1976). Segmentation in urban housing markets. Journal of Urban Economics, 3(2), 146–166. https://doi.org/http://doi.org/10.1016/0094-1190(76)90050-410.1016/0094-1190(76)90050-4
    Search in Google ScholarBack to article
  28. Sirmans, G. S., Macpherson, D. A., &Zietz, E. N. (2005). The Composition of Hedonic Pricing Models. Journal of Real Estate Literature, 13(1), 3–43. http://www.jstor.org/stable/4410350610.1080/10835547.2005.12090154
    Search in Google ScholarBack to article
  29. Soler, I. P., & Gemar, G. (2018). Hedonic price models with geographically weighted regression: An application to hospitality. Journal of Destination Marketing and Management, 9, 126–137. https://doi.org/10.1016/j.jdmm.2017.12.001
    Search in Google ScholarBack to article
  30. Thibodeau, T., & Goodman, A. (2007). The Spatial Proximity of Metropolitan Area Housing Submarkets. Real Estate Economics, 35, 209–232. https://doi.org/10.1111/j.1540-6229.2007.00188.x
    Search in Google ScholarBack to article
  31. Tomal, M. (2021). Housing market heterogeneity and cluster formation: evidence from Poland. International Journal of Housing Markets and Analysis, 14(5), 1166–1185. https://doi.org/10.1108/IJHMA-09-2020-0114
    Search in Google ScholarBack to article
  32. Tomal, M., & Helbich, M. (n.d.). The private rental housing market before and during the COVID-19 pandemic: A submarket analysis in Cracow, Poland. Environment and Planning B: Urban Analytics and City Science, 0(0), 23998083211062908. https://doi.org/10.1177/23998083211062907
    Search in Google ScholarBack to article
  33. Tzioumis, K. (2017). Appraisers and Valuation Bias: An Empirical Analysis. Real Estate Economics, 45(3), 679–712. https://doi.org/https://doi.org/10.1111/1540-6229.1213310.1111/1540-6229.12133
    Search in Google ScholarBack to article
  34. Usman, H., Lizam, M., & Adekunle, M. U. (2020). PROPERTY PRICE MODELLING, MARKET SEGMENTATION AND SUBMARKET CLASSIFICATIONS: A REVIEW. REAL ESTATE MANAGEMENT AND VALUATION, 28(3), 24–35. https://doi.org/10.1515/remav-2020-0021
    Search in Google ScholarBack to article
  35. Usman, H., Lizam, M., & Burhan, B. (2021). A PRIORI SPATIAL SEGMENTATION OF COMMERCIAL PROPERTY MARKET USING HEDONIC PRICE MODELLING. REAL ESTATE MANAGEMENT AND VALUATION, 29(2), 16–28. https://doi.org/10.2478/remav-2021-0010
    Search in Google ScholarBack to article
  36. Walesiak, M., & Dudek, A. (2010). Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 185–192). https://doi.org/10.1007/978-3-642-10745-0_19
    Search in Google ScholarBack to article
  37. Watkins, C. A. (2001). The Definition and Identification of Housing Submarkets. Environment and Planning A: Economy and Space, 33(12), 2235–2253. https://doi.org/10.1068/a34162
    Search in Google ScholarBack to article
  38. Wu, C., & Sharma, R. (2012). Housing submarket classification: The role of spatial contiguity. Applied Geography, 32(2), 746–756. https://doi.org/10.1016/J.APGEOG.2011.08.011
    Search in Google ScholarBack to article
  39. Zhang, H., Zhang, J., Lu, S., Cheng, S., & Zhang, J. (2011). Modeling hotel room price with geographically weighted regression. International Journal of Hospitality Management, 30(4), 1036–1043. https://doi.org/10.1016/j.ijhm.2011.03.010
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/remav-2022-0028 | Journal eISSN: 2300-5289
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Language: English
Page range: 42 - 54
Published on: Dec 9, 2022
Published by: Real Estate Management and Valuation
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
property market segmentation,
geographically weighted regression,
property market analysis
Related subjects:
Business and economics,
Political economics,
Political economics, other

© 2022 Sebastian Gnat, published by Real Estate Management and Valuation
This work is licensed under the Creative Commons Attribution 4.0 License.

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