Austrian Housing Market Analysis

A data-driven analysis using Austrian real estate online platform data
Summary

Table of Contents

1. About The Project
2. Getting Started
   • Installation
   • View Notebook
3. Results

1. About The Project

Real estate information of a particular region or location is not easy to come by. In this notebook we investigate an Austrian real estate data set which has been mined extracted over the course of a week from a large online portal. The data has already been pre-processed and can be geospatial investigated.

This app can be viewed live on Heroku at https://austrian-housing.herokuapp.com/

2. File Structure

app.py main dash app
requirements.txt python modules that will be installed for the web application at build.
/assets this directory is to serve the CSS files and images for the app. charts.py is used for generating the figures.
layout.py defines the html web layout, callbacks.py handles all the callbacks and data_wrangling.py is used for all the data queries and date manipulation.
/data contains the raw data files.
/data/geojson/vienna.geojson geojson files with the geospatial data of Austria.
/nb notebook used for data exploration and analysis.
runtime.txt tells (the Gunicorn HTTP server) which python version to use (only needed for Heroku deployment)
Procfile defines what type of process is going to run (Gunicorn web process) and the Python app entrypoint (only needed for a deployment on Heroku)
.gitignore

3. Installation

Getting Started

• Change the current directory to the location where you want to clone the repository and run:

$ git clone https://github.com/AReburg/Austrian-Real-Estate-Analysis.git

• Make sure that the app is running on the local webserver before deployment. Setup your virtualenv (or don't) and ensure you have all the modules installed before running the app.

Requirements

Install the modules from the requirements.txt with pip3 or conda from a terminal in the project root folder:

pip install -r requirements.txt
conda install --file requirements.txt (Anaconda)

Jupyter Notebook

Executing the notebook is tested on anaconda distribution 6.4.12. with python 3.9.13. To view the rendered geospatial charts of the Jupyter notebook go to nbviewer and copy the link.

4. Usage

Local Web Application

• Run the app from your IDE direct, or from the terminal in the projects root directory: python app.py

• It should be accessible on the browser http://127.0.0.1:8050/

5. Results

The main findings are summarized in a post. Feel free to contact me if you have any questions or suggestions.