Aluno: Sanam Samadani
Resumo
The evolution of the European residential market is notorious over the last ten years.
House prices in the E.U. rose by 30.9 per cent between 2010 and the first quarter of 2021.
The prices of homes in Portugal has risen almost 50 per cent during 11 years. Considering
this previous argument, I propose the following research question: How to predict real
estate prices. In this context, my research aims to analyze the prices’ evolution and
understand the main components impacting the price of real estate. First, using the time
series analysis, I use ARIMA to analyze the prices of real estate and the number of
buildings sold since the first quarter of 2009, almost one year after the great recession in
Portugal, until the third quarter of 2020 which was during the COVID-19 pandemic. The
model was fitted and the prediction line was accurized with an upward trend. The second
approach consists of analyzing the impact of five independent variables on real estate
prices. To understand the most relevant components, regression analysis has been
performed. I used OLS to analyze the impact of independent variables (crime rate,
selected waste rate, tax rate, purchasing power and tourism rate) on real estate prices.
Crime rate and tourism are negatively correlated while purchasing power, selected waste
rate and tax rate are positively correlated with real estate prices. Then, I compared the
accuracy of the result with neural networks and other types of regression analysis. Results
were not much better than with linear regression. It is also essential to consider that this
approach has some limitations, especially regarding the analysis’s granularity. The data
has been collected from INE and PORDATA, the databases of contemporary Portugal, to
construct the models and forecast house prices in Portugal.
Trabalho final de Mestrado