Assessment of changes in the market value of residential real estate in the area of the commissioned transport interchange of the urban toll road

The purpose of this research is to assess the impact of the commissioned toll road running through the city’s residential areas on the market value of residential real estate. The article presents a review of non-traditional methods for assessing real estate value, mainly in foreign publications. The Western High-Speed Diameter (WHSD) is the most significant transport and infrastructure project of the current decade for St. Petersburg. The most inaccessible part of the city on Vasilievsky Island was analyzed, as the example of new and secondary real estate value changes, were examined from August 2015 to December 2017, by the time when the new transport interchange of WHSD in the western part of the island was constructed and put into commission.  For the study, the authors used the data of the Real Estate Bulletin of St. Petersburg at the end of 2015 and 2017, the data of the cadastral assessment of residential real estate of St. Petersburg of 01.01.2015 and 01.01.2018. Main research method is the study of two-dimensional and conditional distributions of random values of bid prices and cadastral values, which allows obtaining estimates of the market value of the real estate that has passed cadastral registration, and estimates of growth rates.  The comparison of prices of offers with cadastral values applied in the article, with a simple and natural speculation of logarithmically normal distribution, allows us to propose a method of assessing the market  value for any property, even if the information about it is not available in the market data. The obtained numerical results showed a rise in the cost of a significant part of the mass-market for the study period up to 18% without discount on the auction, and up to 9% taking into account the discount on the auction. It turned out to be slightly higher than the general change in the prices of proposals that can be found in advertising publications. A significant change (from 50% to 73%) was found in business-class properties, located in the area with significantly changed species characteristics and improved transport accessibility, in the immediate vicinity of the exit from WHSD.  The results, indicating the growth of market value, allowed us to make a general conclusion about the changes in the attractiveness of the area for different segments of the population of the city: both for the mobile middle class, focused on the mass-market, and for buyers of the premium segment, having increased requirements for the real estate. The authors believe that the growth of the market value of real estate in the area of transport interchanges of modern infrastructure projects could be higher in other macroeconomic conditions. At present, the effective demand of the population is obviously not sufficient.  

1. Anselin L., Lozano-Gracia N.Errors in variables and spatial effects in hedonic house price models of ambient air quality. Empirical Economics. 2008; 34; 1: 5–34.

2. Benson E. D., Hansen J.L., Schwartz Jr. A.L., Smersh G.T. Pricing Residential Amenities: The Value of a View. The Journal of Real Estate Finance and Economics. 1998; 16; 1: 55–73.

3. Debrezion G., Pels E., Rietveld P. The impact of rail transport on real estate prices: an empirical analysis of the Dutch housing market. UrbanStud. 2011; 48; 5: 997–1015.

4. Jim C.Y., Chen, W.Y. Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Landscape and Urban Planning. 2006; 78; 4: 422–434.

5. Rivas R., Patil D., Hristidis V., Barr J.R., Srinivasan N. The impact of colleges and hospitals to local real estate markets. Journal of Big Data. 2019; 6; 1.

6. Wena H., Zhanga Y., Zhang L. Assessing amenity effects of urban landscapes on housing price in Hangzhou, China. Urban Forestry & Urban Greening. 2015; 14: 1017–1026.

7. Peterson S., Flanagan A.B.Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal. Journal of real estate research 2009; 31; 2: 147–164.

8. Rafiei M. H., Adeli H. Novel MachineLearning Model for Estimating Construction Costs Considering Economic Variables and Indexes. Journal of construction engineering and management. 2018; 144; 12.

9. Antipov E.A., Pokryshevskaya E.B. Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications. 2012; 39: 1772–1778.

10. Kontrimas V., Verikas A., The mass appraisal of the real estate by computational intelligence. Applied Soft Computing 11. 2011: 443–448.

11. Park B., Baem J.K. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications 42. 2015: 2928–2934.

12. Case K.E., Shiller R.J. Pricesof SingleFamily Homessince 1970: New Indexes for Four Cities. New England Economic Review; 1987: 45– 56.

13. Englund P., Quigley, J.M., Redfearn, C.L. The choise of methodology for computing housing price indexes: comparison of temporal aggregation and sample definition. Journal of real estate finance and economics. 1999; 19; 2: 91–112.

14. Epley D. Assumptions and restrictions on the use of repeat sales to estimate residential price appreciation. Journal of Real Estate Literature. 2016; 24; 2: 275–286.

15. Malpezzi S. Hedonic pricing models: A selective and applied review. In: O’Sullivan T, Gibb K, &editors. Housing economics and public policy: Essays in honor of Duncan Maclennan. Oxford. UK: Blackwell Science. 2002: 67–89.

16. Case B., Quigley J.M. The dynamics of real estate prices. The Review of Economics and Statistics. 1991; 73; 1: 50–58.

17. Englund P., Quigley J.M., Redfearn C.L. Improved price indexes for real estate: Measuring the course ofSwedish housing prices. Journal of Urban Economics. 1998; 44; 2: 171–196.

18. Jones C. House price measurement: The hybrid hedonic repeat-sales method. The Economic Record. 2010; 86; 272: 95–97.

19. Wang F., Zheng X. The comparison of the hedonic, repeat sales, and hybrid models: Evidence from the Chinese paintings. Cogent Economics & Finance. 2018; 6: 1–19.

20. Brunnermeier M.K. Bubbles. In The New Palgrave Dictionary of Economics. L.E. Blume and S.N. Durlauf, eds. New York: Palgrave Macmillan. 2009.

21. Fabozzi F.J., Xiao K. The Timeline Estimation of Bubbles: The Case of Real Estate. Real Estate Economics.2019; 47; 2: 564–594.

22. Fernandez-Kranz D., Hon M.T. A cross-section analysis of the income elasticity of housing demand in Spain: Is there a realestate bubble? Journal of real estate finance and economics. 2006; 32; 4: 449-470.

23. Phillips P.C.B., Shi S.P. Yu J. Testing for Multiple Bubbles: Historical Episodes of Exuberance. International Economic Review. 2015; 56; 4: 1043–1078.

24. Phillips P.C.B., Shi S.P., Yu J. Testing for Multiple Bubbles: Limit Theory of Real Time Detectors. International Economic Review. 2015; 56; 4: 1079– 1134.

25. Laskin M.B., Gadasina L.V. How to determine the cadastral value. Imushchestvennyye otnosheniya v Rossiyskoy Federatsii = Property relations in the Russian Federation. 2018; 3: 42–53. (In Russ.)

26. Rusakov O.V., Laskin M.B., Dzhaksum-bayeva O.I., Stabrovskaya K.YU. Determination of discounts for bargaining according to statistical data. Vestnik grazhdanskikh inzhenerov = Bulletin of civil engineers. 2016; 2: 268-284. (In Russ.)

27. Laskin M.B. Market value adjustment by the pricing factor “facility area”. Imushchestvennyye otnosheniya v Rossiyskoy Federatsii = Property Relations in the Russian Federation. 2017; 8 (191): 86–99. (In Russ.)

28. The official website of the Administration of St. Petersburg [Internet]. Order of the Committee for Property Relations of St. Petersburg dated August 27, 2015 No. 59-p «On approval of the results of determining the cadastral value of premises with an area of less than 3000 square meters in the territory of St. Petersburg». Available from: http://gov.spb.ru/gov/otrasl/kio/documents/inye-dokumenty/4434/. (cited: 05.06.2019). (In Russ.)

29. Portal about real estate in St. Petersburg [Internet]. Real estate prices in St. Petersburg. Request for price dynamics on 07.06.2019, start date: 01.08.2015, end date: 01.01.2018. Available from: https://www.bn.ru/graphs/index.php?singlgraph=main. (cited: 07.06.2019). (In Russ.)

30. St. Petersburg State Budget Institution “City Department of Cadastral Valuation” [Internet]. Report on the determination of the cadastral value of real estate in St. Petersburg No. №1/2018, 2018. Available from: http://www.ko.spb.ru/interim-reports/. (cited: 05.06.2019). (In Russ.)