Night light intensity-based estimation method of average house price of city residences

Abstract

The invention provides a night light intensity-based estimation method of an average house price of city residences. The method includes: collecting and screening average house price data to obtain average house prices of residence and commodity houses of a target city over a plurality of historical years; acquiring remote-sensing DMSP / OLS night light data for preprocessing to obtain regional night light remote-sensing data of the target city over the corresponding historical years, and obtaining average light intensities by counting; carrying out correlation analysis of the average light intensities and the average house prices of the residence and commodity houses of the target city, establishing and comparing a regression model of the average light intensities and the average house prices of the target city, carrying out reasonableness evaluation on the regression model, and estimating the average house price of the city residences in a future year according to the regression model when verification passes. According to the method, on the one hand, a method of spatial analysis is integrated into study of house price statistics, and on the other hand, the remote-sensing image data which is famous for real-time performance and objectivity is also combined; and the method has important guiding significance for city construction and planning.

Description

technical field [0001] The invention belongs to the technical field of remote sensing economic geology application, and proposes a method for estimating the average house price of urban dwellings based on nighttime light intensity. Background technique [0002] Housing prices have always been the most relevant issue for national life and the most concerned by the people; and in terms of national strategy, the real estate industry directly accounts for 10% of GDP. Housing price is an important component of the national economy and a key object of national economic strategy deployment. Based on its importance, many scholars in various industries are devoted to the research and prediction of housing prices. Some scholars start from the perspective of economics and use different methods to prove that the available advance information can predict the future trend of housing prices, but most of them are only based on statistical data and lack spatial analysis. However, current r...

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Problems solved by technology

Some scholars, starting from the perspective of economics, have proved in different ways that advance information can be used to predict the future trend of house prices, but most of them are only based on statistical data and lack spatial analysis. There are also a large number of geographers who use the mathematical and statistical properties of house prices to compare them with geographical locations. However, the current research on urban housing prices does not use more objective remote sensing data

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