real estate data science capstone project simplilearn

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.

Doma: Property Risk Evaluator Take-Home

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.

Real Estate Machine Learning Project For House Price Prediction

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 .

WanderJaunt: Rental Price Analysis Take-Home

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:

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

Real Estate Data Science Capstone Project

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 .

House Price Forecasting Using Zillow Economics Dataset

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.

Identifying Real Estate Opportunities Using Machine Learning

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 Of House Prices

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:

• Significantly improves one’s understanding of the dataset.
• It helps to identify distribution, unique characteristics, or patterns in the dataset.
• It enables one to find outliers, duplicates, or null values.
• 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 .

California Housing Price Prediction Machine Learning Project

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.

Predicting Crimes And Creating A Safety District Index

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.

BONUS: House Prices – Advanced Regression Techniques Competition

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.

More Project Ideas from Interview Query

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

Applying Data Science and Machine Learning to Real Estate

We offer a hands-on course on applying data science, machine learning, and gis to real estate. this is an in-depth version of a masters' level course our instructors ran at a top global university. previous course runs have attracted global participants with backgrounds in institutional real estate, appraisals, proptech, data science, and more., masters' level real estate data science course, course overview.

We are proud to offer one of the world’s first data science, machine learning, and GIS courses dedicated to analyzing, investing in and forecasting property globally.

Through a series of interactive online lectures, hands-on learning and the completion of a key capstone project, you will gain the knowledge and expertise to construct indexes, automate valuations, analyze clusters and forecast time series.

You will gain the necessary skills to utilize large datasets to determine fair transaction prices, forecast future returns and how to analyze locations with geographic information systems (GIS).

Course Modules

What will you learn.

• Data Science Fundamentals (as applicable to all industries) including Python, Pandas, and Scikit-Learn;
• Geographic Information Systems;
• Data Science Methods for Real Estate, including index construction, automated valuation, cluster analysis, and time series forecasting (ARIMA, VAR, and VECM).
• The ability to utilize large datasets to determine fair transaction prices and forecast future returns.

Course Segments & Time Commitment

The program is made up of three (3) main segments. Segments can be taken individually, pending your interest and level of expertise.

Classes consist of interactive, online video conferences. Office hours are one-on-one online video conferences. The capstone project is presented online – students are invited to (but not required to) attend the project presentations of their classmates.

Total Course Time Commitment: ~60 hours - 5 hours/week

Meet your instructors.

Nelson Lau, PhD, CFA

Nelson is the CEO of PropertyQuants Pte. Ltd., a PropTech startup bringing quantitative methods to global real estate. He has a PhD in Decision Sciences from INSEAD, is a CFA Charterholder, and completed his undergraduate work at Columbia University, double majoring in Economics and Mathematics-Statistics. Nelson holds adjunct faculty instructor roles at the Singapore Management University and National University of Singapore's Asian Institute of Digital Finance.

He has published papers in Management Science, Decision Support Systems, and Decision Analysis, one of which received a special recognition award. Nelson started his career as a trader/researcher at R G Niederhoffer Capital Management, an award-winning US hedge fund deploying systematic data-driven medium and low frequency strategies to global markets, and also spent significant time as lead trader at KCG, a leading global high frequency algorithmic trading firm.

He was also a Quantitative Macro Strategist at GIC and Managing Director at a proprietary trading firm (Acceletrade Technologies). Nelson has been investing in international residential real estate in a personal capacity for 10 years, and has a deep interest in bringing more systematic, quantitative, and data-driven approaches to real estate practice.

Xingzhi Cheng, PhD

Xingzhi is CTO of PropertyQuants and has a PhD in Statistical Physics from the National University of Singapore (NUS) and a B.S. in Computer Science from Peking University, with papers published in Physical Review Letters and elsewhere.

He was a postdoctoral research fellow at the Santa Fe Institute and NUS before moving to quantitative trading, where he has 5 years of experience as a researcher, trader, and quantitative developer.

Xingzhi enjoys architecting and developing software and frameworks for systematic and automated research. He’s also developed mobile apps and several different websites in his free time, one of which focused on tracking SGX-listed REITs, and another which analyzed which properties were best to buy or rent for parents in Singapore looking to maximize primary school admission priority for their children. He’s currently excited about building the PropertyQuants platform enabling quantitative and systematic approaches to be applied to real estate investing globally.

Who is this course for?

Participants with a basic foundation in mathematics / statistics at a high school level (GCE ‘A’ level, International Baccalaurate, or equivalent) or higher.

Participants without a background in Python, Pandas, and Sci-kit Learn are required to participate in the bootcamp prior to the course.

The topics we cover are novel and constitute an extension to typical data science courses. Experienced data scientists will gain significant value from participating in all sections of the program.

Questions? Read our FAQ.

UK participants: You can Pay with Knoma ! See our FAQ for more details.

Complete the form below to recieve our full program brochure containing additional course information and pricing.

Course screenshots.

Video Clips

PropertyQuants Achievements

Certified FinTech: AI & Data Analytics Provider

Class of 2019, Colliers Techstars PropTech Accelerator

Grant Recipient, MAS FSTI POC Scheme

Shortlist, Innoleap Call for Solutions (CFS) 2019

Finalist, Built World Technology (BWT) Asia 2019

Learn more about propertyquants.

Data Science Capstone

Skills / knowledge.

• Hadoop framework
• SQL database
• Machine Learning
• Data visualization
• Python and R Programming
• Data Analytics, Statistics

Search code, repositories, users, issues, pull requests...

Provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications

DESCRIPTION A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate. A statistical model needs to be create…

dsaif401/Real-Estate---Capstone-project-Simplilearn

Folders and files, repository files navigation, real-estate---capstone-project-simplilearn, description.

• A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis.
• The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate.
• A statistical model needs to be created to predict the potential demand in dollars amount of loan for each of the region in the USA. Also, there is a need to create a dashboard which would refresh periodically post data retrieval from the agencies.
• The dashboard must demonstrate relationships and trends for the key metrics as follows: number of loans, average rental income, monthly mortgage and owner’s cost, family income vs mortgage cost comparison across different regions. The metrics described here do not limit the dashboard to these few.
• Jupyter Notebook 100.0%

Trending now

Top 50 dbms interview questions and answers in 2024, best data science certifications 2024: chart your data path, business analytics basics: a beginner’s guide, top power bi interview questions and answers in 2024, a guide to the top 50 excel interview questions, top 90+ data science interview questions and answers for 2024, top 30 excel formulas and functions you should know, 50 excel shortcuts key that you should know in 2024, how to use chatgpt & excel for data analytics in 2024, how to use vlookup in excel a step-by-step guide, unlock your data game with generative ai techniques in 2024.

About the Webinar

Unlock the power of data analytics: propel your career to new heights.

Join us on Tuesday, April 30, 2024, for an immersive online Data Analytics Bootcamp in collaboration with Caltech. This bootcamp is designed to accelerate your career in the rapidly evolving field of data analytics, suitable for professionals across all sectors looking to harness the power of data-driven decision-making.

Hosted by Dr. Rick Hefner, the Executive Director at Caltech’s Center for Technology and Management Education (CTME), this bootcamp offers unparalleled expert guidance. With over 40 years of experience in systems development and management education, Dr. Hefner will share invaluable insights that bridge academia and industry practices.

Who Should Attend?

This program is meticulously structured for a diverse audience, including but not limited to:

• Business Professionals: Enhance your strategic decisions through data insights.
• Data Enthusiasts: Dive deeper into the world of data analytics.
• Aspiring Data Analysts: Kickstart your career in data analytics.
• Tech Innovators: Leverage data for technological advancements.
• Career Shifters: Transition into a thriving field with endless opportunities.

Why This Bootcamp Is a Must

Embarking on this bootcamp will equip you with:

• Expert Guidance: Learn from Dr. Rick Hefner and other industry leaders.
• Academic Excellence: Benefit from the academic rigor and cutting-edge curriculum designed by Caltech.
• Practical Experience: Engage in over 20 hands-on projects and capstone projects.
• Networking Opportunities: Connect with peers and professionals in the field.

What You Will Learn:

This comprehensive bootcamp covers essential advanced topics in data analytics, ensuring a robust understanding of:

• Core Analytics Tools: Master Excel, SQL, and data visualization with Tableau.
• Programming Skills: Learn Python and R for data analysis.
• Advanced Data Techniques: Dive into machine learning and predictive analytics.
• Industry Applications: See real-world applications across various industries.
• Career Development: Navigate the job market with the bootcamp’s dedicated career services.

Bonus: Caltech PGP Data Analytics Preview

Get a glimpse into the Post Graduate Program in Data Analytics, which offers:

• Caltech Certification: Receive a prestigious certificate recognized across industries.
• In-depth Learning: Tackle complex data challenges with a comprehensive curriculum and live sessions on latest AI trends.
• Hands-On Projects: Real-world applications through capstone projects and integrated labs.

Join us to transform your understanding of data analytics and carve a niche in this booming industry. Secure your spot today and be part of the future of data-driven decision making.

[CTA] Enroll Now

Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

Recommended Programs

Caltech Data Analytics Bootcamp

*Lifetime access to high-quality, self-paced e-learning content.

Recommended Resources

Data Analytics Basics: A Beginner’s Guide

Caltech Launches Its Data Science Bootcamp in Partnership With IBM

Data Analytics in 2021: A Comprehensive Trend Report

What is Data Analytics and its Future Scope in 2024

A Beginner's Guide to the Top 10 Big Data Analytics Applications of Today

Caltech Launches New Blockchain Bootcamp With Simplilearn

• PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

• Leadership & HR
• Task Management

The MoSCoW Method : Simple task prioritisation in a project

PDF article outlining the MoSCoW method and how to integrate this to your own task management methods.

This Best Practice includes 1 PDF

Wimi Academy offers you this Best Practice for free!

read for free

Add to bookmarks

%product_add_cart_title%

Contribute: $USD help %product_add_cart_label%

No thanks, I just want to %product_skip_link%

View all Eloqoons

New to Eloquens? Create an Account

Create an account

Why should I sign up with LinkedIn? help

Are you using this Best Practice for...

• Business help
• Consulting help
• Student / Non-Profit help

0.0 / 5 (0 votes)

Certificate of publication date

The MoSCoW Method : Simple task prioritisation in a project by Wimi Academy

Version 1 (Original Version): 29/01/2020 14:55 GMT Publication Number: ELQ-61733-1

Add to your library to review

Add to cart to continue reading, add to cart to view the video, please sign-up to download this free best practice.

Have an account?