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Transaction‐Based Office Price Indexes: A Spatiotemporal Modeling Approach

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  • Yong Tu
  • Shi‐Ming Yu
  • Hua Sun

Abstract

This study examines the potential of a two‐order spatiotemporal autoregressive model with a Bayesian heteroskedasticity robust procedure in modeling strata‐titled Singapore office unit transaction prices and in constructing transaction‐based disaggregate office price indexes. The model reduces the problems caused by the infrequent trading of individual commercial properties. However, for those office properties that are located outside the CBD and also for those less frequently transacted, the power of the model in capturing these particular office buildings' price dynamics is limited. The significant differences of the office prices across the various office buildings and submarkets show that the model can capture the variation in office prices and track the timing of capital gains and losses that investors may accrue on spatially distributed office properties more accurately than hedonic or weighted least squares estimates.

Suggested Citation

  • Yong Tu & Shi‐Ming Yu & Hua Sun, 2004. "Transaction‐Based Office Price Indexes: A Spatiotemporal Modeling Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(2), pages 297-328, June.
  • Handle: RePEc:bla:reesec:v:32:y:2004:i:2:p:297-328
    DOI: 10.1111/j.1080-8620.2004.00093.x
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    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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    Cited by:

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    2. Jooyong Shim & Changha Hwang, 2018. "Kernel-based geographically and temporally weighted autoregressive model for house price estimation," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-16, October.
    3. Oğuz Işik & M. Melih Pinarcioğlu, 2006. "Geographies of a silent transition: a geographically weighted regression approach to regional fertility differences in Turkey [Géographie d’une transition silencieuse: une approche des différences ," European Journal of Population, Springer;European Association for Population Studies, vol. 22(4), pages 399-421, December.
    4. Gong Yunlong & de Haan Jan, 2018. "Accounting for Spatial Variation of Land Prices in Hedonic Imputation House Price Indices: a Semi-Parametric Approach," Journal of Official Statistics, Sciendo, vol. 34(3), pages 695-720, September.
    5. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 78-88.
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    7. Ka Shing Cheung & Chung Yim Yiu, 2022. "The economics of architectural aesthetics: Identifying price effect of urban ambiences by different house cohorts," Environment and Planning B, , vol. 49(6), pages 1741-1756, July.
    8. Alice Barreca & Rocco Curto & Diana Rolando, 2018. "Housing Vulnerability and Property Prices: Spatial Analyses in the Turin Real Estate Market," Sustainability, MDPI, vol. 10(9), pages 1-20, August.
    9. Andrea Chegut & Piet Eichholtz & Paulo Rodrigues, 2015. "Spatial Dependence in International Office Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 317-350, August.
    10. Marcel Fischer & Roland Füss & Simon Stehle, 2021. "Local house price comovements," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(S1), pages 169-198, March.
    11. José-María Montero-Lorenzo & Beatriz Larraz-Iribas & Antonio Páez, 2009. "Estimating commercial property prices: an application of cokriging with housing prices as ancillary information," Journal of Geographical Systems, Springer, vol. 11(4), pages 407-425, December.
    12. Marc Francke & Alex Van de Minne, 2021. "Modeling unobserved heterogeneity in hedonic price models," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(4), pages 1315-1339, December.
    13. Jean Dubé & Diègo Legros, 2013. "Dealing with spatial data pooled over time in statistical models," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 1-18, March.
    14. Sinem Güler KANGALLI-UYAR, 2020. "The Matrix Exponential Spatial Specification Approach for Big Datasets: The Analysis of Istanbul Office Market," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(43).
    15. Ilir Nase & Nick van Assendelft & Hilde Remøy, 2019. "Rent Premiums and Vertical Sorting in Amsterdam’s Multi-Tenant Office Buildings," The Journal of Real Estate Finance and Economics, Springer, vol. 59(3), pages 419-460, October.
    16. Ingrid Nappi‐Choulet Pr. & Tristan‐Pierre Maury, 2009. "A Spatiotemporal Autoregressive Price Index for the Paris Office Property Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(2), pages 305-340, June.
    17. Ingrid Nappi‐Choulet & Tristan‐Pierre Maury, 2011. "A Spatial And Temporal Autoregressive Local Estimation For The Paris Housing Market," Journal of Regional Science, Wiley Blackwell, vol. 51(4), pages 732-750, October.
    18. Wieser, Robert, 2009. "Parameterstabilität in hedonischen Bodenpreismodellen [Stability of Parameters in Hedonic Urban Land Price Models]," MPRA Paper 65859, University Library of Munich, Germany.
    19. Rohana Abdul Rahman, 2011. "Variations in Implementing SCM to Minimize Subjectivity and a Future Direction for Malaysia," ERES eres2011_178, European Real Estate Society (ERES).
    20. Thierno Souleymane Barry & Oscar Ngesa & Nelson Owuor Onyango & Henry Mwambi, 2021. "Bayesian Spatial Modeling of Anemia among Children under 5 Years in Guinea," IJERPH, MDPI, vol. 18(12), pages 1-18, June.
    21. Deng, Yongheng & McMillen, Daniel P. & Sing, Tien Foo, 2014. "Matching indices for thinly-traded commercial real estate in Singapore," Regional Science and Urban Economics, Elsevier, vol. 47(C), pages 86-98.

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