Machine Learning for Predicting the Prices of Dwellings in Small and Large Cities of Finland

Aug 3, 2026

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Dwellings rank among the highest-value purchases individuals make and also function as investment assets, making accurate valuation crucial. This master’s thesis studies Finnish dwelling-market dynamics, dwelling pricing, and machine learning approaches, then develops and tests predictive models for large and small cities. Cities are split by resident counts into multiple datasets (all, large-only, small-only), and several algorithms are evaluated and compared. Results identify XGBoost as the strongest predictor, while education-related district factors differ by city size.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages111

Reading Time4 h 40 min

AccessFree to download

Introduction

Machine Learning

Objective and Research Questions

Methodology

The Structure

Dwelling Markets

Finnish Dwelling Market

Dwelling pricing

The Disposition Effect

Features of the Dwelling and Spatial data

Machine Learning

Supervised Machine Learning

Unsupervised Machine Learning

Reinforcement Machine Learning

Pre-processing

Data Integration

Missing data

Outliers

Feature Engineering

Feature Selection

Collinearity & Multicollinearity

Feature Scaling

The Bias-Variance Trade-Off

eXplainable Artificial Intelligence

Machine Learning Algorithms for Dwelling Price Prediction

Linear Regression

Decision Trees

Random Forest and ExtraTrees

XGBoost or Extreme Gradient Boosting

Which Model to Choose

Cross-Validation

Hyperparameter Tuning

Performance Metrics

R-Squared

Santeri Sjöblom Machine Learning for Predicting the Prices of Dwellingsin Small and Large Cities of Finland Master’s Thesis in Information Systems Supervisors: Dr. Xialou Wang Faculty of Social Sciences, Business and Economics, and Law Åbo Akademi University ABSTRACT | Subject: Information Systems | | | --- | --- | | Writer: Santeri Sjöblom | | | Title: Machine Learning for Predicting the Prices of Dwellings in Small and Large Cities of
Finland | | | Supervisor: Dr. Xiaolu Wang | | | Abstract:
Dwellings are one of the most expensive purchases individuals make in their lifetime. Additionally, dwellings can be considered as investment assets. Therefore, it is important to be able to precisely appraise the value of a dwelling, a task which can be achieved through the use of machine learning techniques. The first part of the study is a literature review which covers dwelling market dynamics, pricing of dwellings and use of machine learning in the field. The second part of the thesis presents a study on the Finnish dwelling markets, with a focus on the development of machine learning models for predicting dwelling prices in both large and small Finnish cities. Cities that have over 100,000 residents are considered as large cities and less than 100,000 residents small cities. The research datasets are divided based on the size of the cities into three datasets, with one containing all observations, one containing observations only from large cities, and one containing observations from small cities. The study tests different machine learning algorithms with each dataset and compares the best performing models of each dataset. The results show that the XGBoost algorithm is the best performing algorithm for predicting dwelling prices in Finnish cities. Furthermore, the study found that the importance of residents having a master’s degree in a district decreases in small cities, while it is the most important feature in large cities. | | | Keywords: Machine learning, Linear regression, Decision tree, Random Forest, XGBoost, ExtraTreesRegressor, Predictive analytics, Dwelling price prediction, Finnish dwelling markets | | | Date: 16.4.2023 | Number of pages: 107 | TABLE OF CONTENTS 1 INTRODUCTION .................................................................................................... 1 1.2 Machine Learning ........................................................................................... 1 1.3 Objective and Research Questions ................................................................2 1.4 Methodology ....................................................................................................3 1.5 The Structure................................................................................................... 4 2 DWELLING MARKETS ......................................................................................... 6 2.1 Finnish Dwelling Market.............................................................................. 11 2.2 Dwelling pricing ............................................................................................ 15 2.2.1 The Disposition Effect ............................................................................ 17 2.2.2 Features of the Dwelling and Spatial data............................................... 18 3 MACHINE LEARNING.........................................................................................21 3.1 Supervised Machine Learning .....................................................................22 3.2 Unsupervised Machine Learning.................................................................23 3.3 Reinforcement Machine Learning...............................................................24 3.4 Pre-processing ............................................................................................... 25 3.4.1 Data Integration ..........................................................

FAQs

How does the thesis define small and large Finnish cities?

Cities with over 100,000 residents are treated as large cities, while cities with fewer than 100,000 residents are treated as small cities.

What datasets are used for model development?

The research splits data into three datasets: one with all observations, one with observations from large cities only, and one with observations from small cities.

Which machine learning algorithm performs best for dwelling price prediction?

XGBoost is reported as the best-performing algorithm for predicting dwelling prices in Finnish cities.

Aug 3, 2026

Upload by Patrick