Factors Influencing House Prices in the Real Estate Industry

Background
With the ever growing Australian population which is projected to reach up to approximately 30
million persons in 2029, it is not surprising that several research articles report an increase in
congestion of cities, transport amenities, etcetera if the rate of developing such social amenities
does not adopt to the growing population (CBRE, 2019). However, changes in population come
as both a blessing or at times as a curse depending on the perspective of the viewer. For instance,
considering the real estate industry, increase in population translates to widening marketing
opportunities if the investors know when and how to invest.
Aim
In this paper, we will adopt the use of classification techniques to conduct an analysis of the
factors that influence the prices of houses in the real estate industry.
Methodology
Data mining method
In business and organizational practices, it is often important to put in place ways with
which to handle, process and analyze data so as to gain useful insights. Ideally, evaluation and
prediction of the effects of various activities to the performance of the organization necessitates
that the business put in place measures for data collection, warehousing, and computer
processing is key to extraction of important features that underlay the relationship between a
number of factors. We can therefore define data mining as the “...collection, extraction, analysis,
and statistics of data” (Bose, 2019). This definition of data mining is in line with this paper’s
objective to adopt the use of classification which is categorized under data mining algorithms.

Ideally, data mining is divided into three major components that is: Clustering or Classification,
Association Rules and Sequence Analysis (Kesavaraj & Sukumaran, 2013).
Since our objective is to conduct predictive analytics, we will adopt multiple regression analysis
as our classification method.
Given the previous exercises which involved data cleaning and preparation, in this section we
conduct the actual analysis, report on the results and extend a discussion of the results. In
practice, we conducted multiple linear regression to determine the effect of factors such as:
transaction date, house age, the distance to the nearest market place as well as the number of
convenience stores in the vicinity on the house price per unit area.
Generally, this papers regression model takes the format:
y = α0 + α1 x 1 + ··· + α ix i + £I;
where: y is the price of houses and a0 the regression intercept while ai are the regression
coefficients for the xi variables used in predicting y and £i are the error terms of the model and
the distribution assumption we place on the residuals.
Analysis steps
The initial approach in multiple linear regression is to conduct a correlation analysis so s as to
determine if there is any association between the house price per unit area and other factors
hypothesized to be related to the target attribute.

Figure 1: Correlation Plot
In figure 1 we note significant correlation between the house price per unit area and several other
factors such as: number of convenience stores, longitude, latitude, distance to the nearest MRT
station, transaction date and the age of the house. As such, we can argue that the target variable
has got a relative linear relationship with the predictor variable hence the variables are suitable
for use in fitting the multiple regression model. That is, the predictor variables have a potential
ability to influence the price of the house.
Assumptions
Before implementing our prediction model, beforehand we develop assumptions regarding the
various factors we choose to include for analysis as part of our plan to address the business
objective outlined earlier. The first assumption is that the historical prices of houses presented in
the dataset are measured in a continuous form i.e. it is a continuous variable and do not rely on
discrete measures such as time. Second, we have at least two predictor variables which are either