CN-122023066-A - Real estate data integration analysis method and system based on big data

CN122023066ACN 122023066 ACN122023066 ACN 122023066ACN-122023066-A

Abstract

The invention relates to the technical field of data processing, and particularly discloses a real estate data integration analysis method and system based on big data. The method comprises the steps of realizing multidimensional dynamic analysis of future prices of the target real estate by combining a reference value variation and a utilization rate variation, removing noise data by calculating correlation coefficients of data items and historical price data of the target real estate to obtain effective basic information, determining a first time point when the absolute value of the price variation exceeds a preset fluctuation threshold value as a reference time point, and further determining the price corresponding to the reference time point as a reference price. According to the invention, the noise data with low association degree with price fluctuation is identified and removed, so that the quality of the data input by analysis is ensured, and the accuracy and reliability of price prediction are improved through multidimensional dynamic analysis and data association screening.

Inventors

ZHANG XIANG
LI FANGJIE
JI LONGFEI

Assignees

连云港市不动产交易登记中心

Dates

Publication Date: 20260512
Application Date: 20260202

Claims (10)

1. The real estate data integration analysis method based on big data is characterized by comprising the following steps: determining the reference value variation of the target real property according to the basic information of the target real property and the base reference value; Determining the utilization rate change of the target real property according to the lease record of the target real property; predicting a future price of the target real property based on the reference value variation and the usage rate variation; The method comprises the steps of determining a reference value change quantity of a target real property according to basic information and a base reference value of the target real property, calculating a correlation coefficient of the target real property and historical price data of each item of information in the basic information, determining information with the correlation coefficient larger than a preset correlation threshold value as effective basic information for extracting value influence data, extracting the value influence data in a preset time period from the basic information, updating and calculating a time sequence of the current real property reference value based on the base reference value and the value influence data, and calculating the reference value change quantity according to the time sequence of the current real property reference value.
2. The real estate data integration analysis method based on big data according to claim 1, wherein the updating the time sequence of calculating the current real estate reference value based on the base reference value and the value influence data includes: The method comprises the steps of identifying a data point with the largest value from value influence data as a main influence factor, calculating the difference value between the value of the main influence factor and a basic reference value to obtain a value correction quantity, algebraically summing the basic reference value and the value correction quantity to obtain the current real estate reference value.
3. The real estate data integration analysis method based on big data according to claim 1, wherein the determining the usage rate change of the target real estate according to the lease record of the target real estate includes: Calculating the lease time of each preset time sub-section in a preset statistical period according to the lease record, obtaining a time sequence of the real estate utilization rate by calculating the ratio of the lease time of each preset time sub-section to the duration of the preset time sub-section, and calculating the utilization rate variation according to the time sequence of the real estate utilization rate.
4. The real estate data integration analysis method based on big data of claim 1 wherein predicting the future price of the target real estate based on the reference value variation and the usage rate variation includes: the method comprises the steps of setting weight coefficients for a reference value change amount and a utilization rate change amount respectively, carrying out weighted summation on the reference value change amount and the utilization rate change amount to obtain a comprehensive prediction index, and predicting the future price of the target real estate based on the comprehensive prediction index.
5. Real estate data integration analysis system based on big data is characterized by comprising the following modules: The value analysis module is used for determining the reference value variation of the target real property according to the basic information and the base reference value of the target real property; The utilization rate analysis module is used for determining the utilization rate change of the target real property according to the lease record of the target real property; and the price prediction module is used for predicting the future price of the target real property based on the reference value change and the usage rate change.
6. The real estate data integration analysis system based on big data of claim 5 wherein the determining the reference value variation of the target real estate according to the basic information and the base reference value of the target real estate includes: the method comprises the steps of extracting value influence data in a preset time period from basic information, updating and calculating to obtain a time sequence of the current real estate reference value based on a base reference value and the value influence data, and calculating a reference value change amount according to the time sequence of the current real estate reference value.
7. The real estate data integration analysis system based on big data of claim 6 wherein before extracting value influence data in a preset time period from basic information, further comprising: Calculating a correlation coefficient between each item of information in the basic information and historical price data of the target real property; information with correlation coefficient greater than a preset correlation threshold is determined as effective base information and used for extracting value influence data.
8. The real estate data integration analysis system based on big data of claim 6 wherein the updating the time sequence of calculating the current real estate reference value based on the base reference value and the value influence data includes: The method comprises the steps of identifying a data point with the largest value from value influence data as a main influence factor, calculating the difference value between the value of the main influence factor and a basic reference value to obtain a value correction quantity, algebraically summing the basic reference value and the value correction quantity to obtain the current real estate reference value.
9. The real estate data integration analysis system based on big data of claim 5 wherein determining the usage rate change of the target real estate according to the lease record of the target real estate includes: Calculating the lease time of each preset time sub-section in a preset statistical period according to the lease record, obtaining a time sequence of the real estate utilization rate by calculating the ratio of the lease time of each preset time sub-section to the duration of the preset time sub-section, and calculating the utilization rate variation according to the time sequence of the real estate utilization rate.
10. The real estate data integration analysis system based on big data of claim 5 wherein predicting future prices of the target real estate based on reference value change and usage rate change includes: the method comprises the steps of setting weight coefficients for a reference value change amount and a utilization rate change amount respectively, carrying out weighted summation on the reference value change amount and the utilization rate change amount to obtain a comprehensive prediction index, and predicting the future price of the target real estate based on the comprehensive prediction index.

Description

Real estate data integration analysis method and system based on big data Technical Field The invention belongs to the technical field of data processing, and particularly relates to a real estate data integration analysis method and system based on big data. Background In the real estate industry, the resource allocation efficiency of the real estate market is optimized by integrating and analyzing multi-dimensional real estate data from different channels, and data support is provided for market participants such as investors, developers, intermediaries, consumers and the like. According to the existing real estate data analysis technology based on big data, by processing structured data such as historical transaction prices, areas and age of the building and performing value assessment by adopting a linear regression or shallow model, influence factors such as traffic convenience, community environment quality, peripheral business supporting facility perfection degree, learning area quality and the like are easily ignored, so that the actual value and potential attractive force assessment of real estate are insufficient; In addition, the prior art is rough in description aiming at the demands of users, personalized matching is difficult to realize, screening can be only carried out based on the budget range, the geographic position, the number of rooms and other conditions input by the users, and the mining capability of implicit preference and dynamic demands of the users is lacking, so that targeted recommendation services are difficult to be provided for the users on commute time, house type design style or specific living scenes and the like, the real estate matching efficiency is low, and the blank period of a house source is prolonged. In view of the above, the application provides a real estate data integration analysis method and system based on big data. Disclosure of Invention The invention aims to provide a real estate data integration analysis method and system based on big data, which can comprehensively score the added value of real estate, further output predictive price to a user, and verify and correct the accuracy of results. In order to achieve the above purpose, the invention adopts the following technical scheme: A real estate data integration analysis method based on big data comprises the following steps: determining the reference value variation of the target real property according to the basic information of the target real property and the base reference value; Determining the utilization rate change of the target real property according to the lease record of the target real property; predicting a future price of the target real property based on the reference value variation and the usage rate variation; The method comprises the steps of determining a reference value change quantity of a target real property according to basic information and a base reference value of the target real property, calculating a correlation coefficient of the target real property and historical price data of each item of information in the basic information, determining information with the correlation coefficient larger than a preset correlation threshold value as effective basic information for extracting value influence data, extracting the value influence data in a preset time period from the basic information, updating and calculating a time sequence of the current real property reference value based on the base reference value and the value influence data, and calculating the reference value change quantity according to the time sequence of the current real property reference value. Preferably, the updating the time sequence for obtaining the current real estate reference value based on the base reference value and the value influence data includes: The method comprises the steps of identifying a data point with the largest value from value influence data as a main influence factor, calculating the difference value between the value of the main influence factor and a basic reference value to obtain a value correction quantity, algebraically summing the basic reference value and the value correction quantity to obtain the current real estate reference value. Preferably, the determining the usage rate change of the target real property according to the lease record of the target real property includes: Calculating the lease time of each preset time sub-section in a preset statistical period according to the lease record, obtaining a time sequence of the real estate utilization rate by calculating the ratio of the lease time of each preset time sub-section to the duration of the preset time sub-section, and calculating the utilization rate variation according to the time sequence of the real estate utilization rate. Preferably, the predicting the future price of the target real property based on the reference value variation and the usage rate variation includes: the method comprises the steps