俄罗斯最大城市住宅房地产市场的模拟

IF 0.5 Q3 AREA STUDIES
L. Yasnitsky, V. L. Yasnitsky, A. Alekseev
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引用次数: 1

摘要

现有的用于预测住宅物业市场价值的大规模评估模型和数学工具存在许多缺点,因为它们是针对个别地区开发的。如果不考虑不断变化的经济环境,这些模型很快就会过时,需要不断更新。因此,它们不适合建筑业务优化。该研究旨在为俄罗斯城市创建一个普遍适用的房地产评估系统,而不考虑不断变化的经济形势。这一目标是通过建立一个神经网络来实现的,该网络的输入参数包括建筑和业务数据、地理因素、时间效应以及反映俄罗斯和世界特定地区经济状况的若干指标。为了研究俄罗斯联邦房地产市场的动态,从2006年到2020年的很长一段时间内收集了用于神经网络训练的统计数据。对所开发的系统进行了虚拟机实验。数据显示,在莫斯科,最小面积16平方米的一居室公寓每平方米的单价最高。面积为90平方米的两居室公寓和100平方米的公寓价格最高。M.三室,110平方米。M.四室120平方米。M.五室公寓。在叶卡捷琳堡,总面积为30平方米的两居室公寓每平方米的价格最高;同样适用于110平方英尺。M.三室,130平方米。四间房,150平方米。五室公寓。因此,所提出的系统可用于优化建筑业务。它也可以用于政府机构关心城市房地产市场管理,财产税和住房市场的改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation of Residential Real Estate Markets in the Largest Russian Cities
The existing mass appraisal models and mathematical tools for predicting the market value of residential property have a number of disadvantages, as they are developed for individual regions. Without considering the constantly changing economic environment, these models quickly become outdated and require constant updating. Thus, they are not suitable for construction business optimisation. The study aims to create a universally applicable real estate appraisal system for Russian cities, regardless of the constantly changing economic situation. This goal was achieved through the creation of a neural network, whose input parameters include construction and operational data, geographical factors, time effect, as well as a number of indicators characterising the economic situation in specific regions, Russia and the world. In order to examine the dynamics of real estate markets in the Russian Federation, statistical data for neural network training were collected over a long period from 2006 to 2020. Virtual computer experiments were performed for testing the developed system. They showed that minimum size one-room apartments of 16 square meters have the highest unit cost per square meter in Moscow. Two-room apartments with an area of 90 square meters have the maximum price, as well as 100 sq. m. three-room, 110 sq. m. four-room and 120 sq. m. five-room apartments. In Ekaterinburg, two-room apartments with a total area of 30 square meters have the highest cost per square meter; the same applies for 110 sq. m. three-room, 130 sq. m. four-room and 150 sq. m. five-room apartment. Thus, the proposed system can be used to optimise the construction business. It can be also be useful for government institutions concerned with urban real estate market management, property taxation, and housing market improvement.
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来源期刊
CiteScore
1.80
自引率
20.00%
发文量
23
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