AbstractAbout AuthorsReferences
Russian construction companies are trying to find new markets by implementing updated marketing practices. Understanding the behavior of different customer segments in the process of choosing housing becomes one of the priority tasks of the developer. In the process of choosing residential real estate, consumers of different segments make different requirements for housing and use different criteria for comparing alternatives. The article presents the results of the research conducted by the authors on segmentation of buyers in the residential real estate market. The key criteria for consumers’ choice of housing are structured. Consumer segments are identified according to the level of innovation of consumers and the desired benefits. The analysis of respondents’ preferences, requests, and behavior for the segments selected during the research is presented. The authors used content analysis of previous research by Russian and foreign authors, qualitative research using focus groups, quantitative research in the form of an off-line survey, factor analysis, and cluster analysis as the methods of the research. The obtained research results can be used by Russian real estate developers to create a clearly differentiated offer on the market and increase their competitiveness.
S.V. ALEXANDROVSKIY, Candidate of Sciences (Economy),
E.V. ARTYUSHINA, Candidate of Sciences (Economy) (This email address is being protected from spambots. You need JavaScript enabled to view it.),
D.A. FOMENKOV, Candidate of Sciences (Economy),
M.A. SHUSHKIN, Doctor of Sciences (Economy)
E.V. ARTYUSHINA, Candidate of Sciences (Economy) (This email address is being protected from spambots. You need JavaScript enabled to view it.),
D.A. FOMENKOV, Candidate of Sciences (Economy),
M.A. SHUSHKIN, Doctor of Sciences (Economy)
National Research University Higher School of Economics (25/12, Bolshaya Pecherskaya Street, Nizhniy Novgorod, 603155, Russian Federation)
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1. Shushkin M.A., Aleksandrovskiy S.V., Fomen-kov D.A. Razvitie segmenta individual’nogo zhilishchnogo stroitel’stva: novye formaty i praktiki pozitsionirovaniya [Development of the individual housing construction segment: new formats and positioning practices]. N. Novgorod: NIU RANHiGS, 2018. 180 p.
2. Flambard V. Demand for housing choices in the north of France: a discrete approach // Journal of European Real Estate Research. 2017. Vol. 10. No. 3, pp. 346–365. DOI: 10.1108/JERER-11-2016-0038
3. Guangtong Gu. Hedonic Price Ripple Effect and Consumer Choice: Evidence from New Homes // Journal of Advanced Computational Intelligence and Intelligent Informatics. 2018. Vol. 22, No. 6, pp. 809–816. DOI: 10.20965/jaciii.2018.p0809
4. Iman M., Hamid A., K. N. and S. L. H. A Conjoint Analysis of Buyers’ Preferences for Residential Property // International Real Estate Review. 2012. Vol. 15. No. 1, pp. 73–105. https://www.researchgate.net/publication/254424817_A_Conjoint_Analysis_of_Buyers_Preferences_for_Residential_Property (дата обращения 15.01.2020)
5. Jayasekare A.S., Herath S., Wickramasuriya R., Perez P. The price of a view: Estimating the impact of view on house prices // Pacific Rim Property Research Journal. 2019. Vol. 25, pp. 141–158. DOI:10.1080/14445921.2019.1626543
6. Liu, Zhicheng, Shuai Yan, Jun Cao, Tanhua Jin, Jiabo Tang, Junyan Yang, and Qiao Wang. Bayesian Approach to Residential Property Valuation Based on Built Environment and House Characteristics // IEEE International Conference on Big Data, Big Data. 2018, pp. 1455–1464. DOI: 10.1109/BigData.2018.8622422
7. Worku, Genanew Bekele. House Price Drivers in Dubai: Nonlinearity and Heterogeneity // International Journal of Housing Markets and Analysis. 2017. Vol. 10. No. 3. DOI: 10.1108/IJHMA-06-2016-0048
8. Xu, Yangfei, Qinghua Zhang, Siqi Zheng and Guozhong Zhu. House Age, Price and Rent: Implications from Land-Structure Decomposition // Journal of Real Estate Finance and Economics. 2018. Vol. 56. No. 2. DOI: 10.1007/s11146-016-9596-6
9. Zhan, Dongsheng, Mei Po Kwan, Wenzhong Zhang, Jie Fan, Jianhui Yu and Yunxiao Dang. Assessment and Determinants of Satisfaction with Urban Livability in China // Cities. 2018. Vol. 79, pp. 92–101. DOI:10.1016/j.cities.2018.02.025
10. Zhou, Xiaolu, Weitian Tong and Dongying Li. Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning // ISPRS International Journal of Geo-Information. 2019. No. 8, p. 349. DOI: 10.3390/ijgi8080349
11. Rae A., Sener E. How website users segment a city: The geography of housing search in London // Cities. 2016. No. 52, pp. 140–147. DOI: 10.1016/j.cities. 2015.12.002
12. Kauko T. An analysis of housing location attributes in the inner city of Budapest, Hungary, using expert judgements // International Journal of Strategic Property Management. 2007. No. 11 (4), pp. 209–225. DOI: 10.3846/1648715X.2007.9637570
13. Яговцева М., Потапов Д. Оценка полезности мультиатрибутивного товара на рынке жилой недвижимости // Маркетинг и маркетинговые исследования. 2013. № 3. С. 192–208. https://grebennikon.ru/article-t6gc.html (дата обращения 18.04.20).
13. Yagovsteva M., Potapov D. Evaluating the utility of a multi-attribute product in the residential real estate market. Marketing I Marketingovie issledovaniya. 2013. No. 3, pp.192–208. (In Russian).
14. Габудина А.А., Корпусова Н.С. Особенности механизма принятия решения о покупке на рынке недвижимости и маркетинговых инструментов, оказывающих влияние на потребителей // Экономика и предпринимательство. 2018. № 12 (101). С. 688–695.
14. Gabudina A.A., Korpusova N.S. Features of the mechanism for making a purchase decision in the real estate market and marketing tools that influence consumers. Economica i predprinimatelstvo. 2018. No. 12 (101), pp. 688–695 (In Russian).
15. Сериков Д. Анализ влияния потребительских предпочтений в сфере жилой недвижимости на уровень спроса и темпы продаж девелоперов // Маркетинг и маркетинговые исследования. 2012. № 6. С. 504–513.
15. Serikov D. Analysis of the impact of consumer preferences in residential real estate on the level of demand and sales rates of developers. Marketing I Marketingovie issledovaniya. 2012. No. 6, pp. 504–513. (In Russian).
16. Сироткин В.А., Романова А.Э., Скорин А.В. Фактор демографии в ценообразовании первичного рынка жилой недвижимости // Жилищное хозяйство и коммунальная инфраструктура. 2020. № 1 (12). С. 98–107.
16. Sirotkin V.A., Romanova A.E., Skorin A.V. Demographic factor in pricing of the primary residential real estate market. Zhilishchnoe khozyaistvo i kommunal’naya infrastruktura. 2020. No. 1 (12), pp. 98–107. (In Russian).
17. Трушина Ю. Особенности поведения покупателя жилой недвижимости на основе модели принятия решения // Маркетинг и маркетинговые исследования. 2007. № 6. С. 514–528.
17. Trushina Yu. Features of residential property buyer behavior based on the decision-making model. Marketing I Marketingovie issledovaniya. 2007. No. 6, pp. 514–528. (In Russian).
18. Кучина Е.В., Морозова Л.Ш. Анализ факторов формирования стоимости жилья при переходе к проектному финансированию жилищного строительства // Вестник Южно-Уральского государственного университета. Серия: Экономика и менеджмент. 2018. Т. 12. № 4. С. 54–61.
18. Kuchina E.V., Morozova L.Sh. Analysis of factors that form the cost of housing in the transition to project financing of housing construction. Vestnik uyzhno-uralskogo gosudarstvennogo universiteta. Seriya: economica I menedzhment. 2018. Vol. 12. No. 4, pp. 54–61. (In Russian).
19. Goldsmith R.E., & Hofacker C.F. Measuring consumer innovativeness // Journal of the Academy of Marketing Science. 1991. No. 19 (3), pp. 209–221. DOI: 10.1007/BF02726497
20. Roehrich G. Consumer innovativeness: Concepts and measurements // Journal of Business Research. 2004. Vol. 57. No. 6, pp. 671–677.
21. Moore G.A. Crossing the chasm : marketing and selling disruptive products to mainstream customers. Harper Business Essentials. 2014. 211 p.
22. Jain A.K. Data clustering: 50 years beyond K-means. Pattern Recognition Letters. 2010. No. 31, рp. 651–666. DOI: 10.1016/j.patrec.2009.09.011
23. Kanungo T., Mount D.M., Netanyahu N.S., Piatko C.D., Silverman R., & Wu A.Y. An efficient k-means clustering algorithm: analysis and implementation // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. Vol. 24. No. 7, pp. 881–892. DOI: 10.1109/TPAMI.2002.1017616
For citation: Alexandrovskiy S.V., Artyushina E.V., Fomenkov D.A., Shushkin M.A. Segmentation of residential real estate buyers by desired benefits and their level of innovation. Zhilishchnoe Stroitel’stvo [Housing Construction]. 2020. No. 8, pp. 27–39. (In Russian). DOI: https://doi.org/10.31659/0044-4472-2020-8-27-39