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Temporal transferability of the housing price component of an integrated land use and transportation model

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  • Orvin, Muntahith Mehadil
  • Fatmi, Mahmudur Rahman

Abstract

Housing price is a critical component of integrated urban models (IUMs). The housing market in Canada was affected by COVID-19 including significant price hikes. This poses the question: how transferable are our pre-pandemic travel and land use models to the pandemic period? This question is specifically relevant for IUM’s land use modeling components, such as housing market, since these models tend to make longer-range predictions. With this motivation, this study investigates temporal transferability of housing price micromodel of an IUM. Separate latent segmentation-based autoregressive (LSA) models are developed for pre-pandemic and pandemic periods using sales data from 2016 to 2021 for Central Okanagan region. The key feature of LSA models is to accommodate unobserved heterogeneity, spatial autocorrelation, and temporal correlation. The parameter estimation results of the two models suggest significant differences exist between two periods. For example, buyers are likely to spend more to reside in mixed land use areas during pre-pandemic period. During the pandemic, people are willing to spend more for areas with homogeneous land uses such as residential areas. Furthermore, the pre-pandemic model is applied to pandemic context to examine temporal transferability based on statistical and predictive measures. Results reveal significant differences between the two models. Findings provide empirical evidence on the need for re-estimation or re-calibration of the pre-pandemic model parameters prior to applying for the pandemic period. Developed model will be included in an IUM which is expected to enhance the capacity of IUM to test impacts of socioeconomic shocks like the COVID-19 pandemic on housing market.

Suggested Citation

  • Orvin, Muntahith Mehadil & Fatmi, Mahmudur Rahman, 2024. "Temporal transferability of the housing price component of an integrated land use and transportation model," Land Use Policy, Elsevier, vol. 136(C).
  • Handle: RePEc:eee:lauspo:v:136:y:2024:i:c:s026483772300457x
    DOI: 10.1016/j.landusepol.2023.106991
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    1. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
    2. L. Rachel Ngai & Silvana Tenreyro, 2014. "Hot and Cold Seasons in the Housing Market," American Economic Review, American Economic Association, vol. 104(12), pages 3991-4026, December.
    3. Yuan, Feng & Wu, Jiawei & Wei, Yehua Dennis & Wang, Lei, 2018. "Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China," Land Use Policy, Elsevier, vol. 75(C), pages 225-236.
    4. Kay, Andrew I. & Noland, Robert B. & DiPetrillo, Stephanie, 2014. "Residential property valuations near transit stations with transit-oriented development," Journal of Transport Geography, Elsevier, vol. 39(C), pages 131-140.
    5. Xue, Fei & Yao, Enjian & Jin, Fanglei, 2020. "Exploring residential relocation behavior for families with workers and students; a study from Beijing, China," Journal of Transport Geography, Elsevier, vol. 89(C).
    6. Li-Min Hsueh & Hsi-Peng Tseng & Chang-Chiang Hsieh, 2007. "Relationship Between the Housing Vacancy Rate, Housing Price, and the Moving Rate at the Township Level in Taiwan, in 1990 and 2000," International Real Estate Review, Global Social Science Institute, vol. 10(1), pages 119-150.
    7. Li, Feixue & Li, Zhifeng & Chen, Honghua & Chen, Zhenjie & Li, Manchun, 2020. "An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China," Land Use Policy, Elsevier, vol. 95(C).
    8. Salvati, Luca & Ciommi, Maria Teresa & Serra, Pere & Chelli, Francesco M., 2019. "Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy," Land Use Policy, Elsevier, vol. 81(C), pages 143-152.
    9. William M. Bowen & Brian A. Mikelbank & Dean M. Prestegaard, 2001. "Theoretical and Empirical Considerations Regarding Space in Hedonic Housing Price Model Applications," Growth and Change, Wiley Blackwell, vol. 32(4), pages 466-490.
    10. Song, Yan & Knaap, Gerrit-Jan, 2004. "Measuring the effects of mixed land uses on housing values," Regional Science and Urban Economics, Elsevier, vol. 34(6), pages 663-680, November.
    11. Hensher, David A. & Beck, Matthew J. & Wei, Edward, 2021. "Working from home and its implications for strategic transport modelling based on the early days of the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 64-78.
    12. Gamber, William & Graham, James & Yadav, Anirudh, 2023. "Stuck at home: Housing demand during the COVID-19 pandemic," Journal of Housing Economics, Elsevier, vol. 59(PB).
    13. 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.
    14. Fernandez, Mario Andres & Bucaram, Santiago, 2019. "The changing face of environmental amenities: Heterogeneity across housing submarkets and time," Land Use Policy, Elsevier, vol. 83(C), pages 449-460.
    15. Fox, James & Daly, Andrew & Hess, Stephane & Miller, Eric, 2014. "Temporal transferability of models of mode-destination choice for the Greater Toronto and Hamilton Area," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 41-62.
    16. David Philip McArthur & Liv Osland & Inge Thorsen, 2012. "Spatial Transferability of Hedonic House Price Functions," Regional Studies, Taylor & Francis Journals, vol. 46(5), pages 597-610, August.
    17. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    18. Yasmin, Farhana & Morency, Catherine & Roorda, Matthew J., 2015. "Assessment of spatial transferability of an activity-based model, TASHA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 200-213.
    19. 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).
    20. Paul Waddell, 2011. "Integrated Land Use and Transportation Planning and Modelling: Addressing Challenges in Research and Practice," Transport Reviews, Taylor & Francis Journals, vol. 31(2), pages 209-229.
    21. Acheampong, Ransford A., 2018. "Towards incorporating location choice into integrated land use and transport planning and policy: A multi-scale analysis of residential and job location choice behaviour," Land Use Policy, Elsevier, vol. 78(C), pages 397-409.
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