Market and cadastral data comparison for the real estate market value forecasting

Abstract

The aim of the research is to develop theoretical and methodological approaches to market value forecasting in the real estate market. The relevance of the research is determined by the system-forming place that the real estate market occupies in the economy of the country and regions, affecting the interests of owners of various forms of ownership, construction and development companies, insurance companies, banks. Another aspect that determines the actuality of the study is the discrepancy between well-structured cadastral databases and market data dispersed between different owners of information resources, and the unstructured nature of market data, which in most cases is focused on advertising, rather than on analytical market research.Materials and methods. The study uses a model of a multidimensional logarithmically normal distribution law of the ensemble of prices for residential real estate at equidistant points of time and cadastral value, the ARIMA model for predicting market value, taking into account the features of the logarithmically normal distribution of prices, as a distribution with positive asymmetry. As a statistical material, we used market data on residential real estate published in the periodical press in the period from the end of 2012 to 2018. The volume of samples of weekly publications is 15000-20000 objects; data for 21 quarters (more than five years) was used. As a comparison base, we used data from cadastral registration of real estate objects in Saint Petersburg for 2018. The total volume of the cadastral database of residential real estate in Saint Petersburg (individual apartments) is 2 226734 objects with a fairly complete (and well-structured) set of price-forming factors. The authors propose a method for estimating the most likely movement of the market value for a pre-selected real estate object that has passed cadastral registration and has a cadastral value entered in the register and predicting the market value in the future period.Results. The theoretical significance of the work is the proposed algorithm for estimating the most probable trajectory of the market value of the investigated object, based on the conditional multivariate log-normal distribution for a given value of the cadastral value. A well-developed and studied ARIMA time series forecasting model is applied to the logarithms of the obtained time series, the return from logarithmic prices to real prices is carried out taking into account the peculiarities of the logarithmically normal distribution. Results are compared with median scores and estimates, obtained by average values.Conclusion. The paper shows that the introduction of cadastral value in the Russian Federation opens up new opportunities for analyzing and forecasting market prices, since cadastral databases contain the most complete lists of real estate objects, including the cadastral value, which now, in accordance with the law, must be updated at least once every three years and, as of 2015 and 2018, was determined as the market value, therefore, until the next cadastral assessment, can serve as a basis for constant comparison with market data, which are constantly changing, primarily in the composition of objects.

Keywords

• многомерный логарифмически нормальный закон распределения
• кадастровая стоимость
• рыночная стоимость
• модель arima