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Overview of Data Science at Zillow
- 1. 11 DATA SCIENCE AT ZILLOW Imri Sofer, Senior Data Scientist
- 2. 2 Zillow Group’s mission TO BUILD THE WORLD’S LARGEST, MOST TRUSTED AND VIBRANT HOME-RELATED MARKETPLACE
- 3. 3 Zillow’s marketplace PROPERTY MANAGERS & LANDLORDS BUYERS & SELLERS RENTERS HOMEOWNERS REAL ESTATE AGENTS MORTGAGE PROVIDERS
- 4. 4 For buyers
- 5. 5 For buyers
- 7. 7 Zillow Group two step business model: 1. Make amazing products to attract users 2. Professionals pay to show case themselves.
- 8. 8 For buyers
- 9. 9 The largest portfolio of real estate brands CONSUMER BRANDS BUSINESS BRANDS
- 10. 10 Zillow Group’s audience continues to grow MONTHLY UNIQUE USERS Quarterly average (Millions) 0 20 40 60 80 100 120 140 160 180 Seasonal peak of 171M Unique visitors in May 2016
- 11. 1111 Why is data science important to Zillow? Because Zillow is data
- 12. 12 Zillow is data - Our product is driven by data - The largest most comprehensive housing data (Breadth and depth). - Over 65 million have been updated by users. - Our product generates data - 2MM Reviews of agents. - More than 300,000 lender reviews. - 1TB of user activity every day. - Data is our product - Users come to Zillow because they trust our housing data. - Users want to find a trusted agent, and lender that provide great rates and services. - We provide data for free for academic/institutional researchers. - Zillow.com/data – free consumer data (Zillow home value index is available at a monthly frequency for the nation through states, to neighborhoods.)
- 13. 13 Data Science and Engineering at Zillow Clam Bake Beach Day, Aug 2016, at Golden Gardens Park in Seattle, WA
- 14. 14 Machine Learning at Zillow Home Valuation • Zestimate • Zestimate Forecast • Zillow Home Value Index • Rent Zestimate • Zillow Rent Index • Pricing Tool • Best Time to List B2B • Ad Campaigns • Agent segmentation • Search Engine Marketing (SEM) Computer Vision • Videos • Photos User Profiles • Persona Predictions • Journey location prediction • Lender Recommendations Recommendations • Home recommendation • Similar homes • New regions to explore • Explain recommendations
- 15. 15 Machine Learning at Zillow • Example page Home Valuation • Zestimate • Zestimate Forecast • Rent Zestimate • Pricing Tool • Best Time to List • Zillow Home Value Index • Zillow Rent Index example page
- 16. 16 Zestimate Goals: • High Accuracy • Low Bias • Independent • Stable over time. • Robust to outliers. • High coverage (Over 100 million homes currently) • Able to respond to user fact changes
- 17. 17 Challenges with the Zestimate • Some listings are missing features: How do we deal with missing data? • Some listings have corrupted features (e.g. 28 bathrooms): How do we identify those? • Some sale prices do not reflect the value of the home(e.g. a parent sales to his child): how do we deal with outliers? • Feature engineering: How can we translate previous sales to meaningful features? • How do we identify the places where the model needs to be improve?
- 18. 18 Machine Learning at Zillow Home Valuation • Zestimate • Zestimate Forecast • Zillow Home Value Index • Rent Zestimate • Zillow Rent Index • Pricing Tool • Best Time to List Computer Vision • Videos • Photos
- 19. 19 Computer Vision at Zillow • Images and videos play a big role in helping people buy/rent homes • Recent deep-learning advancements for CV
- 20. 20 Let Zillow See • As of now, our Zestimates are mainly based on location and size of the properties and they do not consider the quality. • Tax assessment might carry house quality information up to some extent but that’s not enough. • For example, an interior upgrade would not change the tax assessment in most cases if not all
- 21. 21 • We train a deep convolutional neural network (CNN) to estimate quality. Deep Convolutional Neural Network Zestimate
- 22. 22 Image quality scores (prediction) [0-3] [3-7] [7-10]
- 23. 23 Machine Learning at Zillow Home Valuation • Zestimate • Zestimate Forecast • Zillow Home Value Index • Rent Zestimate • Zillow Rent Index • Pricing Tool • Best Time to List Computer Vision • Videos • Photos Recommendations • Home recommendation • Similar homes • New regions to explore • Explain recommendations
- 25. 25 Home Recommendations • Our goal is to show users the homes that are relevant to them. Email When viewing a home Ranking search results
- 26. 26 Email Recommendation • Goal: Take past user activity and generate relevant recommendations for new and existing listings. • Challenges: • How do we transform user activity into a vector of features? • What do we want to optimize for? Clicks? Dwell time? Saves? • What should we do when users don’t have a browsing history (cold start)? • How can we scale the model to rank 2.5MM homes for 50M buyers? Most recommendation algorithms are not built for this problem (Netlifx has 5000 movies in its catalog)
- 27. 27 • user_id listing_id like • 12 5 1 • 12 34 0 • 12 567 1 • 144 5 0 • 144 34 0 • 1550 567 1
- 30. 30 How can we generate meaningful features? • Date user_id listing_id f1 f2 ... f50 like • 2017-01-02 12 5 0.89 0.3 0.6 0 • 2017-01-09 12 34 0.90 0.1 0.1 0 • 2017-01-29 12 567 0.82 0.8 0.1 1 • 2017-01-02 144 5 0.19 0.9 0.9 0 • 2017-02-20 144 34 0.40 0.3 0.8 0 • 2017-02-03 1550 567 0.99 0.9 0.8 1
- 31. 31 Machine Learning at Zillow Home Valuation • Zestimate • Zestimate Forecast • Zillow Home Value Index • Rent Zestimate • Zillow Rent Index • Pricing Tool • Best Time to List B2B • Ad Campaigns • Agent segmentation • Search Engine Marketing (SEM) Computer Vision • Videos • Photos User Profiles • Persona Predictions • Journey location prediction • Lender Recommendations Recommendations • Home recommendation • Similar homes • New regions to explore • Explain recommendations
- 32. 32 Tools • Spark (Scala and Python) • R • Python (numpy, scipy, sklearn, pandas) • Random forest • Linear, logistic, quantile regressions. • Deep neural nets. • Matrix Factorization • Etc. • AWS
- 33. 33 Zillow Core Values • Own it. • Turn on the Lights. • ZG is a Team Sport. • Move Fast. Think Big. • Winning is Fun. • Act With Integrity
- 34. 3434 We’re hiring! • Data Scientist, Computer Vision and Deep learning • Software Engineer, Machine Learning • Data Scientist, Machine Learning • Internship opportunities across Analytics - Glassdoor reviews: Top 10 in Seattle Business Magazine 100 Best Companies (#3) - Glassdoor’s Employees’ Choice Best Places to Work; Glassdoor’s Best Benefits and Perks; www.zillow.com/jobs www.zillow.com/data-science
Editor's Notes
- Roadmap for today: Overview of company, data, and culture Introduce the Data Science and Engineering team and the problems we try to solve Leave time at the end for general Q&A
- Zillow was founded ten years ago with a simple but incredibly ambitious mission: To build the world’s largest, most trusted and most vibrant home-related marketplace. What this means is that we’re a company which creates a marketplace, and a marketplace has consumers and practitioners., We’re not a brokerage, not an agent, not an MLS; We are creating a marketplace – a place where consumers and producers congregate to conduct commerce with one another.
- For buyers: - We help buyers understand the state of the marketplace, what can they afford provide them information about each and every listing recommend homes for them, and alert them when a new relevant listing came to market Help them to price a listing. Help them to chose an agent based on rating and number of sales. For sellers: Help them to price their home. See how many people view it online. Connect them to an agent to help them sell, or let them sell by themselves.
- For agents, lenders: - provide a way to connect with new clients, and to demonstrate their success.
- A few years ago Zillow went into rentals and today it’s the leading site in this category in the US.
- Here on the bottom right we can see where agents have an opportunity to connect with buyers.
- Ten years ago, we were just Zillow, but our brand portfolio grew over time and reflects our mission. Each brand is striving to empower the consumers through transparency. Zillow, Trulia and Hotpads focuses on homes and rentals nation wide. StreetEasy and Naked Apartments focus on NYC. Business brands: Mortgage quotes/rates (Mortech), transaction platform (dotloop)
- Huge user base. 30MM rental shopper per month. First in real estate class - double from our largest competitor (Realtor.com ) 78% Market share of all mobile exclusive visitors to real estate category. In July - Half a billion homes were viewed on Zillow Mobile (270/second) (?????) Mortgages – 35 million requests in last year
- Steven
- There are 21 people in the picture. We are actually 48 people now, and have 12 open positions. Our mission: We attack Zillow’s DS challenges. Today I’ll talk about the
- Start with demo Zestimate is what made Zillow so famous. It started on day 1, and it what differentiates us from our competitors. <go over list> Zillow Home Value index is a economic index derived from the Zestimate. Today it is used by large financial institution, organization and municipalities to understand the real estate market and help decision making. This means that Zestimate is not only helping individuals to value homes, it also help decision makers to understand the housing market.
- This is a supervised learning problem. Each home in our dataset, has a set of features associated with it and its sale price. Our goal is to predict the sale price using the features.
- David
- Netflix page is very personalized and tailored to the user interests. Each row gives a different way to organize movies. The first and created by the same model, which gets a collection of movies with a single attributes and rank them according to the user viewing habits. The second row is from a completely different model the rank similarity between movies. All these rows are ordered by a third model. - We would like to simplify the home buying experience and make it as easy a choosing a movie on Netflix.
- Each type of recommendation answers different needs. Email – We would like to send users alerts when their dream home comes on the market, or show them homes that they might wouldn’t consider. The challenge is how not to spam. When viewing a home, showing other similar homes that the user might like. When ranking search results, we need to chose the most relevant homes to go to the top of the list.
- In recommendation what we usually have is a set of user-item pairs and a corresponding label. The idea is that if we can predict whether a user would like a listing we could make good recommendations. This seems is a supervised learning problem. In real life it’s much more complicated. - How do we know if a user like an item? Most users don’t explicitly tells us. For example, most users don’t rate movies and like videos on youtube. Even when user tells us, it does not necessary means what we want it to mean. For instance, a user might not like a listing, but it was very relevant for him because at this stage she’s just exploring the market and she would like to understand what she can afford. So listings for homes we will never buy help us understand our options. The challenge with recommendation is that we never solve for the problem that we would like to solve. We only solve for a surrogate problem. So part of our work is to find the best surrogate problem to solve.
- We have a very large catalog. No of users is on the same order as the number of Items. No popular items. Block diagonal matrix
- To complicate things, we have features associated with the listings. And we have user activity. How can we translate that to features that are predictive of the outcome.
- Shown mission/brands/data – how do we get there Zillow culture - people Share people you like David – ZG is a team sport, turn on the lights (anonymous questions, wikis, open discussion) Steven –Winning Is Fun – competition, Move Fast Think Big (hackweeks)