In this paper, the author first analyzes the major factors affecting housing prices with
Spearman correlation coefficient, selects significant factors influencing general housing …

The tremendous speedup in computing in recent years, the low data storage costs of today,
the availability of “big data” as well as the broad range of free open-source software, have …

This paper studies how the presence of universities and hospitals influences local home
prices and rents. We analyze the data on ZIP code level and on the level of individual …

Purpose The purpose of this study is to carry out a comparative analysis of four machine
learning models such as linear regression, decision trees, k-nearest neighbors and support …

Urban systems' dynamics are the result of two intertwined processes that operate at different
rhythms: their physical structure and underlying social processes. This paper suggests a …

Urban scholars have made great advances to understand the reciprocal relations between
households and their immediate environments as a means for the creation of efficient urban …

Industrial cluster is a common phenomenon in the process of industrialization. It is an
external scale formed by agglomeration effect. Cluster enterprises based on their …

This paper aims to present the implementation of two machine learning algorithms, Linear
Regression and Lasso Regression for the task of predicting the price of the house located in …

A large part of economic growth is driven by housing investment. The availability of better
data permits more precise estimates of house price in developed countries, while the same …

The house price index is essential for assessing the housing market conditions and
predicting future changes in the market. However, the two main methods for calculating the …