CubiCasa Property Data Solution
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New level of accuracy for real estate data Home price valuation is a key part of home selling and buying process. Valuation is also fundamental criteria for banks to make mortgage decision and it is also used in foreclosures. Automated valuation models (AVM) do not take into account property condition or layout characteristics which can have a major impact on valuation. CubiCasa real estate data platform has quantified information of property characteristics which enables a totally new level of details and analytics for various real estate use cases. More information: Jarmo Lumpus, founder & Head of Product, jarmo@cubicasa.com
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CubiCasa Property Data Solution
- 1. Property data solution for real estate
- 2. New level of accuracy for real estate data Home price valuation is key part of home selling and buying process. Valuation is also fundamental criteria for banks to make mortgage decision and it is also used in foreclosures. Automated valuation models (AVM) do not take into account property condition or layout characteristics which can have major impact to valuation (https://www.zillow.com/zestimate). CubiCasa real estate data platform has quantified information of property characteristics which enables totally new level of details and analytics for various real estate use cases.
- 3. CubiCasa property data use cases 1. Fundamental part of the property reports 2. New criterias and features for home search 3. Data for Comparable Market Analysis (CMA) 4. More accurate AVMs (Automated Valuation Models) 5. Find price sensitive factors through data analytics 6. Validate existing real estate data 7. Property characteristics standard through RESO data dictionary
- 4. CubiCasa property data solution Convert millions of BIM models with AI tech + Automated conversion Automated input acquiring = Property Data Platform (RESO compliant) Technology which makes floor plan input cost minimal AI tech to convert BIM models automatically Huge amount of property BIM models accessed through API
- 5. What information property BIM model can include? How many rooms and their type Size of each room Number of windows and doors Kitchen, dining, living room concept Appliances (e.g. DW) Terrace/balcony/deck Bedrooms with W.I.C or en-suite bath Compass direction Layout form factor and shape Special things like: loft, corner apartment, storage space, ... CubiCasa solution enables totally new kind of analytics and finding price sensitive characteristics. There is no need to ask homeowners to update their property characteristics, like Zillow and others do.
- 6. CubiCasa property data to become a standard (RESO)
- 7. Shorter sales cycle for properties in platform Property in platform Ranks better in search results Gets more views Shorter sales cycle
- 8. All major players provide AVM ● Hometrack acquired by Zoopla for $150M (2017) ● Platinum Data Solutions acquired by Mercury Network (2016) ● FNC acquired by CoreLogic for $475M (2015) ● LPS (BKFS) acquired by Fidelity National $2.9B (2014) ● Retsly acquired by Zillow (2014) ● DataQuick acquired by CoreLogic (2013)
- 9. Status as of today >1 million original floor plans acquired from US + = >95 000 BIM models converted > 4500 NYC property BIM models available in platform
- 10. How we monetize the property data? Product CubiCasa property data platform is accessed only through API Target segment Companies providing home search, property report, comparables or AVM Target market US Business model SaaS What’s the secret source? Data reuse: convert once - sell it to many, multiple times! What other use cases this data enables? Smart property search for all kinds of real estate apps e.g. AirBnB
- 11. What this property data is worth? There is no direct references available but if we look at the property data service providers House Canary (www.housecanary.com), the main service being the value report This company raised $33M A-round Jan 2017 Property details $0.19…$0.25 Value $0.74…$1.00 Value report $4.20…$5.00 SaaS $54/month/10 reports or PRO $1000/month for unlimited access
- 12. Example: Zillow improves Zestimate AVM with neural network - More “human-friendly” way to interpret images and data - Labeling of all kinds of features (kitchen, materials, sizes etc) - Adding indoor space data complements property valuations - Normalization of data - there is no standard how floor plans or images should be interpreted - Most importantly: built to reduce valuation error rate and improve home search accuracy & results Sources: Inman News, informationweek.com, housely.com. Current error rate 4.5%.
Notes de l'éditeur
- Original text removed in case of copyrights, copypaste below: Zillow develops ‘neural network’ to identify spaces like humans would The new technology is expected to increase the accuracy of the Zestimate Key Takeaways Zillow announced the development of a "neural network" that allows computers to dissect and identify features in an image the same way humans do. This network will be used to better identify and tag a home's features, such as granite countertops, a large backyard and wood floors. This high-tech tagging system is expected to increase the accuracy of Zillow's Zestimates. Human brains can capture and analyze hundreds of thousands of data points in seconds — which is one of many reasons why a human appraiser often puts online valuations to shame in terms of accuracy. Zillow is building its own version of human intelligence into its listings and online valuation tool, the Zestimate — dubbed a “neural network,” it will allow machines to identify such niceties as the type of cabinet and countertops used in the kitchen and adjust the home’s value accordingly. Computer vision technology teaches computers to view, dissect and understand images, and then automatically identify and tag features that would be attractive to a potential buyer, such as granite countertops, high ceilings and french doors. In March, RealScout founder and CEO Andrew Flachner explained his company’s use of the same technology, saying: “Homebuyers don’t measure ‘home’ in square feet. “Instead, my buyer clients described their home preferences in terms of large backyards, open floor plans and lots of natural light. Flachner noted real estate’s usage of the technology is still in its early days, but argued that with correct use, it can substantially streamline and quicken the online home search process. “Agents and brokers who effectively integrate these technologies will be able to better cater to their clients’ needs, and will naturally be in higher demand,” he said. Trulia has already employed computer vision technology; Inman will update this story with more information about how or whether Trulia’s computer vision tools will be different from Zillow’s neural network when it becomes available. Zillow Group Chief Economist Stan Humphries told The Wall Street Journal the “neural network” will be in use by Q1 2017, and it is expected to lower the current 4.5 percent Zestimate valuation error rate.