Moscow housing vacancy rate estimation

DOI: 10.35595/2414-9179-2025-3-31-273-291

View or download the article (Rus)

About the Authors

Tatiana I. Baltyzhakova

ITMO University,
49, Kronverksky dr., bldg. A, St. Petersburg, 197101, Russia,
E-mail: tibaltyzhakova@itmo.ru

Valeria V. Palich

Moscow institute of architecture (state academy),
11/4, Rozhdestvenka str., bldg. 4, Moscow, 107031, Russia,
E-mail: office@marhi.ru

Pavel O. Syomin

Perm State University,
15, Bukireva str., Perm, 614068, Russia,
E-mail: ntsp@ya.ru

Yulia O. Chutova

Independent researcher,
Nizhny Novgorod,
E-mail: julia.o.rodikova@gmail.com

Abstract

The research presented in the article is devoted to the formation of the methodology and analysis of the housing vacancy rate in Moscow. The study defines the working concept of “housing vacancy”, which, in the context of the study, means that a residential property is not someone’s primary place of residence, regardless of the subjective assessment of vacancy by the owner of the property. The methodology is based on the use of open data from the Territorial Development Fund (TDF), the results of the Russian census, the report on the results of the state cadastral assessment of the city of Moscow for 2023, and the register of housing inventory objects. In the course of implementing the proposed methodology, the vacancy rate was estimated for 60 % of Moscow’s residential housing objects. The study showed that in many districts the high vacancy rate of the housing stock can be explained by the impact of integrated territorial development projects. The results were aggregated by municipal districts and examined for spatial autocorrelation and clusters of high or low vacancy rates. A more detailed analysis showed that the highest vacancy rate is characteristic of the New Moscow districts, as well as some districts influenced by the process of integrated territorial development. The calculated indicators of local spatial autocorrelation indicate the presence of areas with high concentration of both high and low levels of housing vacancy rate, which can serve as an indirect measure of the urban environment quality. The methodology proposed by the authors has a high degree of reproducibility and general applicability, as it uses only data from open sources and has a detailed description of the software code for the calculations.

Keywords

housing vacancy rate, residential property, open data, GIS, spatial analysis

References

  1. Aleksandrova O.A., Alekseeva O.A. Fundamentals of Economic Behavior and Welfare of the Population: Motives, Factors, Expectations. Moscow: Federal Center of Theoretical and Applied Sociology of Russian Academy of Sciences, 2024. 351 p. (in Russian). DOI: 10.19181/monogr.978-5-89697-433-8.2024.
  2. Flas M., Halleux J.-M., Cools M., Teller J. Identifying Housing Vacancy Using Data on Registered Addresses and Domestic Consumption. Housing Studies, 2022. V. 39. P. 1317–1339. DOI: 10.1080/02673037.2022.2119212.
  3. Golovin A.V., Guzd T.V., Vitkov G.V., Karaselnikova I.V., Kosolapov N.A. Planning of Sprawl. Spatial Policy of Russian Cities. Moscow: Publishing House of the HSE, 2021. 248 p. (in Russian). DOI: 10.17323/978-5-7598-2578-4.
  4. Gorodnichev A.V., Skrebkova E.V. Uneven Development of Moscow in the Context of the Existing Spatial Structure of a Post-Socialist Megacity. Urban Studies and Practices, 2022. V. 7. No. 1. P. 85–105 (in Russian). DOI: 10.17323/usp71202285-105.
  5. Hoekstra J., Vakili-Zad C. High Vacancy Rates and Rising House Prices: The Spanish Paradox. Journal of Economic and Human Geography, 2010. V. 102. No. 1. P. 55–71. DOI: 10.1111/j.1467-9663.2009.00582.x.
  6. Lee J., Newman G., Lee C. Predicting Detached Housing Vacancy: A Multilevel Analysis. Sustainability, 2022. V. 14. No. 2. P. 922. DOI: 10.3390/su14020922.
  7. Li J., Guo M., Lo K. Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data. Sustainability, 2019. V. 11. No. 20. P. 5722. DOI: 10.3390/su11205722.
  8. Liu X., Li Z., Fu X., Yin Z., Liu M., Yin L., Zheng W. Monitoring House Vacancy Dynamics in The Pearl River Delta Region: A Method Based on NPP-VIIRS Night-Time Light Remote Sensing Images. Land, 2023. V. 12. No. 4. P. 1–21. DOI: 10.3390/land12040831.
  9. Newman G., Lee R.J., Gu D., Park Y., Saginor J., Van Zandt S., Li W. Evaluating Drivers of Housing Vacancy: A Longitudinal Analysis of Large U.S. Cities from 1960 to 2010. Journal of Housing and the Built Environment, 2019. V. 34. P. 807–827. DOI: 10.1007/s10901-019-09684-w.
  10. Sayuda K., Hong E., Akiyama Y., Baba H., Tokudomi T., Akatani T. Accuracy of Vacant Housing Detection Models: An Empirical Evaluation Using Municipal and National Census Datasets. Transactions in GIS, 2022. V. 26. P. 3003–3027. DOI: 10.1111/tgis.12992.
  11. Yang P., Pan J. Estimating Housing Vacancy Rate Using Nightlight and POI: A Case Study of Main Urban Area of Xi’an City, China. Applied Sciences, 2022. V. 12. P. 12328. DOI: 10.3390/app122312328.
  12. Yang D., Xiao B., Lu X., Jia X., Li X., Han F., Sun L., Shi F., Khumvongsa K., Li J., Duan X. Assessment and Driving Factor of Housing Vacancies in Shandong Peninsula Urban Agglomeration Based on Multi-Source Remote Sensing Data. Heliyon, 2023. V. 9. No. 9. P. e16837. DOI: 10.1016/j.heliyon.2023.e16837.
  13. Yue X., Wang Y., Zhao Y., Zhang H. Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China. ISPRS International Journal of Geo-Information, 2022. V. 11. P. 349. DOI: 10.3390/ijgi11060349.

For citation: Baltyzhakova T.I., Palich V.V., Syomin P.O., Chutova Yu.O. Moscow housing vacancy rate estimation. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 3. P. 273–291. DOI: 10.35595/2414-9179-2025-3-31-273-291 (in Russian)