10 Real Estate Data Science Projects

In the KPMG Global PropTech Survey 2018, 49% of respondents identified artificial intelligence, big data, and data analytics as the technologies expected to have the most significant long-term impact on the real estate industry.

Are you interested in participating in the data-driven development of the real estate industry? Do you want to discover patterns in the real estate market? Here are ten awesome real estate machine learning projects to get you started.

Purpose:

We would like you to use a Jupyter (python) notebook to work with a slice of this data. You’ll get a sense of the type of questions that we deal with at States Title, and we’ll get a sense of your data science approach.

How you can do it:

Write python code that allows you to stand up a nationwide title insurance company:

• It should read the files default_notices.csv, train_property_data.csv, and test_property_data.csv, described below.
• It should append a new column, risk, to the test_property_data.csv file, which represents your prediction of the overall title risk for the property. This column should behave in such a way that properties with lower risk are predicted to be more profitable than properties with higher risk.
• You are at complete freedom to set the method for measuring risk, and the column itself can contain any real-valued number that satisfies part.

Want to learn how to build and evaluate a model’s performance and predictive power using machine learning regression algorithms? Developing a house price prediction model is a great way to start.

There’s a ton of accessible housing data online, e.g., sites like Zillow and Airbnb, and these datasets are perfect for executing this type of project. Zillow’s free datasets are a popular choice; the Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.

The project consists of two phases: Developing a model and training the data, then applying different regression algorithms and testing for the best fit.

How you can do it: This tutorial by Victor Roman takes you through all the steps of collecting, cleaning, and exploring housing data, then developing a machine learning model and applying different regression algorithms, and evaluating the model’s performance.

A more straightforward approach can be building a linear regression model and using K-fold cross-validation to measure the model’s accuracy. VarunSonavni uses this method with Python to examine the Bengaluru House price dataset on Kaggle in this tutorial.

Data on short-term rental prices and occupancy is very important to WanderJaunt. It helps inform us how competitors are pricing, which influences our own pricing strategy and helps us benchmark our own occupancy and revenue per available room against similar properties.

In addition, it provides key inputs to the decision of what locations and markets we enter and what types of properties can be the most profitable.

Questions to answer:

1. What data would you exclude from analysis for being unreliable or potentially a block instead of an actual booking?
2. What is a good approach to estimating occupancy and revenue per unit?
3. Which month appears to be more profitable? April or May?
4. How much more revenue do places with 3 bedrooms make vs. places with 2 bedrooms?
5. What are any other interesting insights you may have found?

Real estate developers and investors have always sought to understand where to acquire property and when to trigger development. They look for places where the housing prices are low, and the facilities (shops, restaurants, parks, hotels, etc.) and social venues are nearby.

According to the latest report by the prestigious Mckinsey consulting firm, big data and data analytics is the way to analyze the ton of nontraditional valuables that affect house prices and quickly identify potential investment opportunities.

How you can do it: This real estate data science capstone project tutorial by Muhammad Taha Khan uses publicly available data from Wikipedia and Foursquare API to develop a machine learning model that can cluster the data mentioned above visually for the large city of London.

The model uses an unsupervised learning K-means algorithm to cluster the boroughs and folium Python library to visualize and display the resulting clusters.The project includes housing data sets, and you can also check the code in its GitHub repository.

Clients, real estate agents, home trading firms, and other investors often have biased assumptions about whether home values ​​in a particular area will rise or fall. The recent UK and Australian-based studies suggest valuations between two professionals can differ by up to 40%.

So instead of making potentially biased or inaccurate assumptions, it’s better to use statistical methods to predict the value of homes over time.

The latest application combining an extensive database of traditional and nontraditional data, was used to forecast the three-year rent per square foot for multifamily buildings in Seattle. These machine-learning models predicted rents with an accuracy rate that exceeded 90 percent.

How you can do it: Follow Uma Gajendragadkar’s tutorial Using the Zillow Economic Dataset and Time Series Modeling with ARIMA to see how this project performs.

In 2018, Skyline AI, a NewYork-based commercial real estate investment startup that uses machine learning algorithms to identify possible investment opportunities, acquired two multifamily residential complexes in Philadelphia for $26 million.

According to their PR release, they claim that they closed the deal with a price that was 12% under its expected value. “We saw that similar assets that had already been renovated were able to increase their rents by about $300 per unit,” Skyline AI CEO Guy Zipori.

Such a remarkable performance convinced lots of real estate investors that maybe they should be increasingly relying on machine learning. But developing machine learning algorithms that can accurately identify these opportunities is not easy, as the variables that affect pricing are not always easy to recognize.

How you can do it: This project develops a property price classification model using a current decade dataset from publicly available data from the Volusia County, Florida, Real Estate Appraisers website.

Algorithms utilize powerful machine learning, namely logistic regression, random forest, voting classifier, and XGBoost. The developed model can help real estate investors, mortgage lenders, and financial institutions make informed decisions.

You can use the study by Alejandro Baldominos to learn more about accomplishing such a daunting task. Published Public case studies are available at Cornell for more in-depth analysis.

Exploratory data analysis is a core skill for any aspiring data scientist. Learning how to explore and analyze data is a necessary process not only for training a particular model but also for various other purposes.

Advantages of performing an EDA:

1. Significantly improves one’s understanding of the dataset.
2. It helps to identify distribution, unique characteristics, or patterns in the dataset.
3. It enables one to find outliers, duplicates, or null values.
4. It represents the data visually in a more understandable manner.

How you can do it: This project uses a house prices dataset from Kaggle to perform such analysis in a simple and easy-to-understand way. You can also complete your research using this weekly updated USA housing dataset.

Experimenting with accurate data is always the best way to learn about the fundamental challenges you face in the workplace. In this real-data project tutorial, Gurupratap S Matharu goes through an end-to-end real estate machine learning project to predict house prices in California using advanced regression.

How You Can Do It: The tutorial covers all the steps from understanding the business goals and acquiring the dataset, processing the data and experimenting with different ML models to find the best fit, and finally launching, monitoring, and maintaining the system.

If you like it, you can try to recreate the same project using different housing datasets from Kaggle.

Living in a safe community is something everyone is actively seeking. The Seattle Open Data Project provides access to the Seattle City Police Department’s 911 emergency response as a part of its open data project.

Using this data, you can cluster and map different types of crime and organize them by severity. Then overlay them on a population density-based crime density map to construct a model that predicts crimes and groups regions based on a safety index.

The cleaned dataset and code used to build this project are available in Jay Feng’s GitHub repository, and you can follow his blog post for more details on how to perform this type of analysis.

Kaggle – the well-known data science community – is running an ongoing competition for data science students who have completed an online machine learning course and want to expand their skills before trying out featured competitions.

The competition is an excellent opportunity to build and test all data scientist skills. The competition has a clear goal, a public leaderboard, and numerous housing datasets and tutorials.

If you want more projects to develop your skills further, try our new Takehomes, where you solve more prolonged problems step-by-step with notebooks from different companies.

Takehomes will help you build your data science skills, including Python, SQL, and machine learning, and try out projects used in high-profile companies.

Additionally, you can look at other data science project lists and datasets from Interview Query:

• Top 10 Regression Datasets and Projects
• 31 Free Datasets for Your Next Project
• 12 Machine Learning Projects (Beginner to Advanced)
• 10 Python Projects with Source Code
• 21 Data Analytics Project Ideas and Datasets
• Top 12 Classification Machine Learning Projects