Denmark Property Valuation Analysis

Research Question

We are set to explore the relationship between socioeconomic factors and property valuations within the context of Denmark's municipalities. Our research is split into two main questions:

1. How can we predict high-demand areas and property value, both historically and presently?
2. How can we model and forecast property demand trends over the next 5 years?

Data Sources

Our analysis will leverage data from various sources:

• Property Listing Data

  • Sites such as Home.dk, Boliga.dk, and Boligsiden.dk
  • Acquisition method: Webscraping

• Historical Property Data

  • Public sources like Vurderingsstyrelsen, BBR, and DAWA
  • Acquisition method: API

• Social/Economic Data

  • Historical data from sources such as Danmarks Statistik (DST) and DinGeo

Data Analysis & Methodology

The project's data analysis will adhere to a typical analytical pipeline:

1. Data Collection: Tools such as web-scraping and fetching data via API will be used.
2. Data Preparation & Processing: Conducted using Python.
3. Exploratory Data Analysis (EDA): To comprehend the nature of the regressions we'll be dealing with.
4. Machine Learning (ML) Analysis: Our primary tool for predicting property values based on aggregated socioeconomic data. Depending on data distribution, we'll pick a suitable regression model. K-fold cross validation will be employed to optimize our training and test sets. Additionally, we'll explore L1, L2, or elastic net regressions based on multicollinearity in our dataset.
5. Sharing & Visualization: Our findings will be represented through intuitive visualizations and concise data tables or dashboards.