House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis

Abstract

Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers, and prediction of house price trend as well as business decisions made by enterprises. By virtue of machine learning and statistical theory, the house price in Zhaoqing City from 2010 to 2020 will be researched, and the house price prediction model of Zhaoqing City will be constructed in this paper with several variables including GDP, proportion of tertiary industry, income of urban residents, fiscal revenue, land price, investment volume in real estate development, permanent resident population, population density, and proportion of urban population in net migration. First of all, the methods of correlation analysis will be utilized, to select variables that are highly correlated with house price data based on correlation coefficients. Then, the model will be constructed for predicting the house price on the basis of multiple linear regression analysis that is conducted with selected variables. Finally, the prediction model will be adjusted gradually based on data with different correlations selected from available data, to realize better imitative effect and more precise predictive effect and select optimum prediction model. By means of the above model, the house prices of Zhaoqing City in 2021 and beyond will be predicted accurately, with preferable fitting effect and prediction effect.

Association rule‐based multilevel regression pricing and artificial neural networks based land selling price prediction based on market value

“…Multiple linear regression is used for prediction. 14 House price prediction is performed through spatial linear hedonic models for cities. Flexible spatiotemporal model is developed based on the spatiotemporal characteristics and analyzed the impact in middle-small cities.…”

“…Correlation analysis is performed with collected data. Multiple linear regression is used for prediction 14 . House price prediction is performed through spatial linear hedonic models for cities.…”

“…The proposed utilitarian association rule mining model of guideline pricing in the article considers 30 different attributes when determining the rule, and these attributes are based on customer land buying patterns gathered from questionnaires. The questionnaires in the attributes dataset include: (1) water supply, (2) drainage and sewage, (3) recharging aquifers, (4) commercial district, (5) mixed-use housing, (6) the principal residence commercial district institution, (8) Small scale industry, (9) Large scale industry (10) recreation urbanization, (11) residential construction,(12) non-urbanization,(13,14) dangerous location, (15) commercial activity,(16) an arterial road, (17) a sub-atrial road, (18) a collector office,(19) a major road, (20) an airport, (21) a bus stop,(22) a bus terminus, (23) a college distance, (24) a commercial relationship, (25) a government school, (26) a private school, (27) a hospital, (28) an industrial relation activity, (29) information technology-parks, (30) a park, (31) a posh area, (32) a major road, (33) a slum area, (34) a temple, (35) a metro rail, (36) a guideline. In the aforementioned 36 attributes, attributes 1 through 18 describe whether or not infrastructure exists in a specific location, while attributes 19 through 35 describe the distance between a location's land and another location, and are obtained from a GIS-based model builder.…”

Analysis of the Factors Influencing Housing Prices in Yunnan and Guizhou Based on Linear Regression

“…Moreover, some researchers make predictions about the trends of property prices in the future, based on statistics from specific cities all around the world. Chen used the multiple linear regression analysis and some data related to Zhaoqing's real estate market to create the model to predict the price of real estate in the future [5]. Certainly, it is significant to find some factors that would influence housing price in different areas.…”

AdaBoost Classification for Predicting Residential Habitation Status in Mount Merapi Post-Eruption Rehabilitation

“…Moreover, the AdaBoost algorithm has been advantageous in the context of human mobility and residential patterns. A study by Chen (2022) utilized the AdaBoost algorithm to predict urban housing prices, demonstrating the algorithm's effectiveness in analyzing complex housing data [15]. In another study, Burger et al (2019) used AdaBoost to model and predict the residential relocation patterns after the devastating flood in the Midwest United States, highlighting the algorithm's potential to forecast complex human behaviors [16].…”