ISSN 1995-2732 (Print), 2412-9003 (Online)
УДК 332.1+330.4
DOI: 10.18503/1995-2732-2026-24-1-176-191
Аннотация
Пространственная вариация цен зависит от влияния различных факторов. В существующей литературе эмпирические исследования по этой теме значительно различаются в зависимости от вида товаров, целей исследований и их методологии, рассматриваемому уровню пространственной иерархии. В данной работе проведен анализ исследований, посвященных пространственной вариации цен наиболее распространенных товаров, их основным детерминантам, применяемым методам анализа, а также наличию или отсутствию пространственного компонента в изменении цен. Используя систематический подход и метод PRISMA, мы рассмотрели 278 статей и провели контент-анализ на основе базы данных Scopus и ресурса журналов Tandfonline. Результаты показали, что в современной литературе преобладают исследования, связанные с вариацией цен на жилье и землю. Наиболее значимыми факторами, влияющими на разницу в ценах на жилье, являются архитектурные характеристики, местоположение (географические переменные), социально-экономические факторы, наличие локальных удобств или недостатков, а также особенности сделок по покупке жилья. Детерминанты вариации цен на землю тесно связаны с типом участка: цены на жилую землю зависят от доступности инфраструктуры и удаленности от различных социальных объектов (образование, здравоохранение). Наиболее распространенной пространственной единицей анализа в исследованиях цен на землю является страна, тогда как в работах по ценам на жилье доминирует городской масштаб. Вклад данного исследования в научную литературу заключается в следующем. Во-первых, мы анализируем статьи из нашей выборки с точки зрения различных типов цен и методов, используемых для оценки влияния факторов на пространственную вариацию цен на разных уровнях пространства. Полученные результаты могут служить основой для исследователей, изучающих пространственную динамику цен. Во-вторых, мы выявили, что цены на продовольственные товары представляют собой наиболее перспективное направление для изучения сезонных и временных аспектов пространственной вариации цен.
Ключевые слова
пространственная вариация цен, пространственная волатильность, цены на жилье, цены на землю
Исследование выполнено при финансовой поддержке гранта РНФ № 24-28-00774, https://rscf.ru/project/24-28-00774/
Для цитирования
Пространственная вариация цен в региональном контексте: систематический обзор зарубежной литературы / Красносельская Д.Х., Тимирьянова В.М., Прудников В.Б., Гайнцева Е.С. // Вестник Магнитогорского государственного технического университета им. Г.И. Носова. 2026. Т. 24. №1. С. 176-191. https://doi.org/10.18503/1995-2732-2026-24-1-176-191
1. Girardin E., Sall Ch. A.T. Inflation Dynamics of Franc-Zone Countries Determinants, Co-movements and Spatial Interactions // Open Economies Review. 2018, no. 29(2), pp. 295–320. https://doi.org/10.1007/s11079-017-9456-x
2. Spatial quantitative analysis of garlic price data based / Wu G., Zhang C., Liu P., Ren W., Zheng Y., Guo F., Chen X., Higgs R. // Computers, Materials & Continua. 2019, no. 58(1), pp. 183–195. https://doi.org/10.32604/cmc.2019.03792
3. Cunha A.M., Loureiro R. Housing price dynamics and elasticities: Portugal's conundrum // National Accounting Review. 2024, no. 6(1), pp. 75−94. https://doi.org/10.3934/NAR.2024004
4. Gray D. Housing market activity diffusion in England and Wales // National Accounting Review. 2023, no. 5(2), pp. 125–144. https://doi.org/10.3934/nar.2023008
5. Kirikos D. G. An evaluation of quantitative easing effectiveness based on out-of-sample forecasts // National Accounting Review. 2022, no. 4(4), pp. 378–389. https://doi.org/10.3934/nar.2022021
6. Динамика пространственной корреляции цен на овощи и фрукты / Тимирьянова В.М., Лакман И.А., Красносельская Д.Х., Столь А.В. // Пространственная экономика. 2023. Т. 19. № 2. С. 94−125. https://doi.org/10.14530/se.2023.2.094-125
7. Spatial dependence of average prices for product categories and its change over time: Evidence from daily data / Timiryanova V., Lakman I., Prudnikov V., Krasnoselskaya D. // Forecasting. 2022, no. 5(1), pp. 102–126. https://doi.org/10.3390/forecast5010004
8. Gluschenko K. Distribution dynamics of Russian regional prices // Empirical Economics. 2016, no. 51(3), pp. 1193–1213. https://doi.org/10.1007/s00181-015-1039-3
9. Ступникова А.В. Пространственное поведение в Российской Федерации в 2003−2012 гг. // Экономические и социальные перемены: факты, тенденции, прогноз. 2014. Т. 3. № 33. С. 248–261. https://doi.org/10.15838/esc/2014.3.33.19
10. Sinelnikov-Murylev S.G., Perevyshin Y.N., Trunin P.V. Inflation differences in the Russian regions: An empirical analysis // Economy of Region. 2020, no. 16(2), pp. 479–493. https://doi.org/10.17059/2020-2-11
11. Кириллов А.М. Инфляция цен на продовольственные товары в регионах России: пространственный анализ // Пространственная экономика. 2017. № 4. С. 41–58. https://doi.org/10.14530/se.2017.4.041-058
12. Иванова В.И. Конвергенция цен на рынке зерна: исторический аспект // Пространственная экономика. 2015. № 3. С. 34–56. https://doi.org/10.14530/se.2015.3.034-056
13. Drivers of grain price volatility: a cursory critical review / Santeramo F.G., Lamonaca E., Contò F., Nardone G., Stasi A. // Agricultural Economics. 2018, no. 64(8), pp. 347–356. https://doi.org/10.17221/55/2017-agricecon
14. Gilbert C.L., Morgan C.W. Food price volatility // Philosophical Transactions of the Royal Society B: Biological Sciences. 2010, no. 365(1554), pp. 3023–3034. https://doi.org/10.1098/rstb.2010.0139
15. Li N., Li R., Nuttapong J. Factors affect the housing prices in China: a systematic review of papers indexed in Chinese Science Citation Database // Property Management. 2022, no. 40(5), pp. 780–796. https://doi.org/10.1108/pm-11-2020-0078
16. Implications for long COVID: A systematic review and meta-aggregation of experience of patients diagnosed with COVID-19 / Zheng X., Qian M., Ye X., Zhang M., Zhan C., Li H., Luo T. // Journal of Clinical Nursing. 2024, no. 33(1), pp. 40–57. https://doi.org/10.1111/jocn.16537
17. Ten S., Raga J.A., Aznar F.J. Epibiotic fauna on cetaceans worldwide: A systematic review of records and indicator potential // Frontiers in Marine Science. 2022, no. 9. https://doi.org/10.3389/fmars.2022.846558
18. Meerow S., Newell J.P., Stults M. Defining urban resilience: A review // Landscape and Urban Planning. 2016, no. 147, pp. 38–49. https://doi.org/10.1016/j.landurbplan.2015.11.011
19. Heyman A.V., Law S., Berghauser P.M. How is location measured in housing valuation? A systematic review of accessibility specifications in hedonic price models // Urban Science. 2018, no. 3(1). https://doi.org/10.3390/urbansci3010003
20. Rapsomnikis G., Hallam D., Conforti P. Market Integration and Price Transmission in Selected Food and Cash Crop Markets of Developing Countries: Review and Applications // Commodity Market Review. 2003, pр. 51−75. Режим доступа: https://openknowledge.fao.org/server/api/core/bitstreams/a41bf51e-e53f-4b0e-afff-dc0aa4c7f5fa/content (date of access 17.06.2025)
21. Majumder A., Ray R., Santra S. The spatial price map of India drawn using pseudo unit values // Indian Growth and Development Review. 2020, no. 13(3), pp. 537–560. https://doi.org/10.1108/igdr-09-2019-0092
22. Mishra A., Ray R. Spatial Variation in Prices and Expenditure Inequalities in Australia // Economic Record. 2014, no. 90(289), pp. 137–159. https://doi.org/10.1111/1475-4932.12109
23. Bergeaud A., Raimbault J. An empirical analysis of the spatial variability of fuel prices in the United States // Transportation Research Part A: Policy and Practice. 2020, no. 132, pp. 131–143. https://doi.org/10.1016/j.tra.2019.10.016
24. Payne M.T., Smith M.G. Price determination and efficiency in the market for water rights in New Mexico’s Middle Rio Grande Basin // International Journal of Water Resources Development. 2013, no. 29(4), pp. 588–604. https://doi.org/10.1080/07900627.2012.730670
25. Xiao H., Kant S., Xie Y. A spatial hedonic stumpage analysis of standing timber auctions in Jiangxi Province of China // Forest Policy and Economics. 2018, no. 96(3), pp. 63−74. https://doi.org/10.1016/j.forpol.2018.08.004
26. Evaluation of spatial heterogeneity in the sensitivity of on-street parking occupancy to price change / Pu Z., Li Z., Ash J., Zhu W., Wang Y. // Transportation Research Part C: Emerging Technologies. 2017, no. 77, pp. 67–79. https://doi.org/10.1016/j.trc.2017.01.008
27. Latinopoulos D. Using a spatial hedonic analysis to evaluate the effect of sea view on hotel prices // Tourism Management. 2018, no. 65, pp. 87–99. https://doi.org/10.1016/j.tourman.2017.09.019
28. How to better incorporate geographic variation in Airbnb price modeling? / Jiang Y., Zhang H., Cao X., Wei G., & Yang Y. // Tourism Economics. 2023, no. 29(5), pp. 1181-1203. https://doi.org/10.1177/13548166221097585
29. Chiou L., Kafali E. Nilay, Rysman M. Internet use, competition, and geographical rescoping in Yellow Pages advertising // Information Economics and Policy. 2020, no. 52, 100867. https://doi.org/10.1016/j.infoecopol.2020.100867
30. Mohammed I., Guillet B.D., Law R. Modeling dynamic price dispersion of hotel rooms in a spatially agglomerated tourism city for weekend and midweek stays // Tourism Economics. 2019, no. 25(8), pp. 1245−1264. https://doi.org/10.1177/1354816619826829
31. Lee C., Park K. Analyzing the rent-to-price ratio for the housing market at the micro-spatial scale // International Journal of Strategic Property Management. 2018, no. 22(3), pp. 223−233. https://doi.org/10.3846/ijspm.2018.1416
32. Geng B., Bao H., Liang Y. A study of the effect of a high-speed rail station on spatial variations in housing price based on the hedonic model // Habitat International. 2015, no. 49, pp. 333–339. https://doi.org/10.1016/j.habitatint.2015.06.005
33. Wang B. Is Walmart the same as ten years ago? A non-parametric difference-in-differences analysis of Walmart development. Regional Science and Urban Economics. 2023, no. 99, pp. 103863. https://doi.org/10.1016/j.regsciurbeco.2022.103863
34. De UK, Vupru V. Housing demand and its determinant in a small town in India. J Urban Plan Dev. 2021, no. 147(1), 05020036. DOI:10.1061/(asce)up.1943-5444.0000647.
35. Gao Y., Feng C. Impact of mixed land use on housing prices, spatial differentiation and implications: Empirical analysis based on Qingdao // Applied Spatial Analysis and Policy. 2023, no. 16(4), pp. 1345–1370. https://doi.org/10.1007/s12061-023-09514-1
36. Drivers of spatial change in urban housing submarkets / Costello G, Leishman C, Rowley S., Watkins C. // The Geographical Journal. 2019, no. 185(4), pp. 432–446. https://doi.org/10.1111/geoj.12303
37. Osland L., Thorsen I.S., Thorsen I. Accounting for local spatial heterogeneities in housing market studies // Journal of Regional Science. 2016, no. 56(5), pp. 895–920. https://doi.org/10.1111/jors.12281
38. Banzhaf H.S., Farooque O. Interjurisdictional housing prices and spatial amenities: Which measures of housing prices reflect local public goods? // Regional Science and Urban Economics. 2013, no. 43(4), pp. 635–648. https://doi.org/10.1016/j.regsciurbeco.2013.0
39. Using GIS and Hedonic in the modelling of spatial variation of housing price in Xiamen city / Li Y., He L., Xu W., Wang H., He Z. // International Review for Spatial Planning and Sustainable Development. 2013, no. 1(4), pp. 29–42. https://doi.org/10.14246/irspsd.1.4_29
40. Chica-Olmo J., Cano-Guervos R., Chica-Rivas M. Estimation of housing price variations using spatio-temporal data // Sustainability. 2019, no. 11(6), 1551. https://doi.org/10.3390/su11061551
41. Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China / Yang F., Wu J., Wei Y.D., Wang L. // Land Use Policy. 2018, no. 75, pp. 225–236. https://doi.org/10.1016/j.landusepol.2018.03.045
42. Zou Y. Air Pollution and Housing Prices across Chinese Cities // Journal of Urban Planning and Development. 2019, no. 145(4), 04019012. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000517
43. De U.K., Vupru V. Housing demand and its determinant in a small town in India // Journal of Urban Planning and Development. 2021, no. 147(1), 05020036. https://doi.org/10.1061/(asce)up.1943-5444.0000647
44. De Bruyne K., Van H.J. Explaining the spatial variation in housing prices: an economic geography approach // Applied Economics. 2013, no. 45(13), pp. 1673–1689. https://doi.org/10.1080/00036846.2011.636021
45. Rauf M.A., Weber O. Housing sustainability: The effects of speculation and property taxes on house prices within and beyond the jurisdiction // Sustainability. 2022, no. 14(12), 7496. https://doi.org/10.3390/su14127496
46. Fingleton B., Fuerst F., Szumilo N. Housing affordability: Is new local supply the key? // Environment and Planning A: Economy and Space. 2019, no. 51(1), pp. 25−50. https://doi.org/10.1177/0308518X18798372
47. How Effective are Policy Interventions in a Spatially-Embedded International Real Estate Market? / Duan K., Mishra T., Parhi M., Wolfe S. // The Journal of Real Estate Finance and Economics. 2019, no. 58(4), pp. 596−637. https://doi.org/10.1007/s11146-018-9654-3
48. Zang T., Gu H. State-space modelling of housing sentiment for regressing changes of real estate prices following short-term control policy in China // Sustainability. 2023, no. 15(16), 12660. https://doi.org/10.3390/su151612660
49. Coulson N.E., McCoy S.J., McDonough I.K. Economic diversification and the resiliency hypothesis: Evidence from the impact of natural disasters on regional housing values // Regional Science and Urban Economics. 2020, no. 85, 103581. https://doi.org/10.1016/j.regsciurbeco.2020.103581
50. Mou Y., He Q., Zou Bo. Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability // Sustainability. 2017, no. 9(10), 1826. https://doi.org/10.3390/su9101826
51. Oikarinen E., Engblom J. Differences in housing price dynamics across cities: A comparison of different panel model specifications // Urban Studies. 2016, no. 53(11), pp. 2312−2329. https://doi.org/10.1177/0042098015589883
52. Choi Y., Jeung I., Park J. Comparative Analysis of Spatial Impact of Living Social Overhead Capital on Housing Price by Residential type // KSCE Journal of Civil Engineering. 2021, no. 25(3), pp. 1056-1065. https://doi.org/10.1007/s12205-021-1250-z
53. Bagheri B., Shaykh-Baygloo R. Spatial analysis of urban smart growth and its effects on housing price: The case of Isfahan, Iran // Sustainable Cities and Society. 2021, pp. 68, 102769. https://doi.org/10.1016/j.scs.2021.102769
54. Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices / Wu C., Ren F., Hu W., Du Q. // International Journal of Geographical Information Science. 2018, no. 33(3), pp. 489–511. https://doi.org/10.1080/13658816.2018.1545158
55. Chasco C., Le Gallo J., López F.A. A scan test for spatial groupwise heteroscedasticity in cross-sectional models with an application on houses prices in Madrid // Regional Science and Urban Economics. 2018, no. 68, pp. 226–238. https://doi.org/10.1016/j.regsciurbeco.2017.10.015
56. Mimis A., Rovolis A., Stamou M. Property valuation with artificial neural network: the case of Athens // Journal of Property Research. 2013, no. 30(2), pp. 128−143. https://doi.org/10.1080/09599916.2012.755558
57. Seifert S., Hüttel S. Is there a risk of a winner’s curse in farmland auctions? // European Review of Agricultural Economics. 2023, no. 50(3), pp. 1140–1177. https://doi.org/10.1093/erae/jbad002
58. Modeling fine-scale residential land price distribution: An experimental study using open data and machine learning / Zhang P., Hu S., Li W., Zhang C., Yang S., Qu S. // Applied Geography (Sevenoaks, England). 2021, no. 129, 102442. https://doi.org/10.1016/j.apgeog.2021.102442
59. Spatio-Temporal Nonstationary Effects of Impact Factors on Industrial Land Price in Industrializing Cities of China / Yang S., Hu S., Li W., Zhang C., Song D. // Sustainability. 2020, no. 12(7), 2792. https://doi.org/10.3390/su12072792
60. Ahlfeldt G.M., Nitsch V., Wendland N. Ease vs. noise: Long-run changes in the value of transport (dis)amenities // Journal of Environmental Economics and Management. 2019, no. 98, 102268. https://doi.org/10.1016/j.jeem.2019.102268
61. Chen W., Shen Y., Wang Y. Does industrial land price lead to industrial diffusion in China? An empirical study from a spatial perspective // Sustainable Cities and Society. 2018, no. 40, pp. 307−316. https://doi.org/10.1016/j.scs.2018.04.019
62. Rahardjo N., Marhaento H. Spatio-Temporal Visualization of Land Price Change in the Greater Yogyakarta City, Java, Indonesia // International Journal of Geoinformatics. 2019, no. 14(4), pp. 47−59. Режим доступа: http://journals.sfu.ca/ijg/index.php/journal/article/view/1233 (date of access 17.06.2025)
63. Christensen G., Bronchetti E.T. Local food prices and the purchasing power of SNAP benefits // Food Policy. 2020, no. 95, 101937. https://doi.org/10.1016/j.foodpol.2020.101937
64. Achoja F., Gbigbi T. Taze Domatesin Mekansal ve Mevsimsel Fiyat Varyasyonları: Nijerya Örneği // Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi. 2019, no. 29(2), pp. 330–338. https://doi.org/10.29133/yyutbd.464931
65. Van Campenhout B., Lecoutere E., D’Exelle B. Inter-temporal and spatial price dispersion patterns and the well-being of maize producers in southern Tanzania // Journal of African Economies. 2015, no. 24(2), pp. 230–253. https://doi.org/10.1093/jae/ejv002
66. Bekkerman A., Taylor M. The role of spatial density and technological investment on optimal pricing strategies in the grain handling industry // Review of Industrial Organization. 2020, no. 57(1), pp. 27–58. https://doi.org/10.1007/s11151-019-09725-2
67. Katchova A.L., Sant’Anna A.C. Impact of Ethanol Plant Location on Corn Revenues for U.S. Farmers // Sustainability. 2019, no. 11(22), 6512. https://doi.org/10.3390/su11226512
68. Kitenge E.M., Morshed A.K. Price convergence among Indian cities: The role of linguistic differences, topography, and aggregation // Journal of Asian Economics. 2019, no. 61, pp. 34–50. https://doi.org/10.1016/j.asieco.2019.02.002