Second-hand house price measuring and calculating method

Abstract

The invention discloses a second-hand house price measuring and calculating method, and relates to the technical field of real estate application, and the method comprises the steps: S1, obtaining house resource samples, and collecting the macroscopic indexes and microscopic indexes of buildings to which the house resource samples belong; S2, carrying out cleaning to remove invalid house resourcesamples, and merging similar house resource samples into the same building; S3, calculating the average price of the building; S4, dividing the buildings into different categories according to the average price and longitude and latitude information of the buildings, and calculating the average price of each category; S5, fitting the average price of each building in the regression model by takingthe macroscopic indexes, the microcosmic indexes and the average price of each category as characteristics, and generating a house price measurement model; s6, inputting the name of the second-hand house to be calculated into the house price calculation model, and calculating the house price of the second-hand house by the house price calculation model according to the macroscopic index and the microcosmic index of the house to which the second-hand house belongs and the average price of the category to which the second-hand house belongs. The technical problem to be solved by the invention is to improve the accuracy of a second-hand house price measurement result.

Description

technical field [0001] The invention relates to the technical field of real estate applications, in particular to a method for measuring and calculating second-hand housing prices. Background technique [0002] With the development of big data technology and artificial intelligence technology, the working methods and methods of the real estate evaluation industry have undergone significant changes. Traditional evaluation methods are affected by factors such as insufficient data dimensions, inconsistent data, and untimely data updates, resulting in manpower, The time cost is too high. Based on this, more and more institutions are now using big data and artificial intelligence calculation methods to assist in evaluation. [0003] The document with the publication number CN108647987A discloses a housing asset valuation method, which includes dividing the second-hand housing valuation area, collecting the housing transaction prices of each community in the second-hand housing v...

AI Technical Summary

Problems solved by technology

However, in practical applications, this technology still has the following technical defects: 1. The impact on housing prices has not been studied from a richer dimension, such as population structure, macroeconomics, etc.

2. In the evaluation model, only the linear relationship between the influencing factors and the house price is considered, and the existence of the nonlinear relationship is not considered

3. The establishment method of the model is more based on human experience, and the hidden connection of the data cannot be found

These defects will lead to poor accuracy of the measurement results

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