Data Annotation for ML Projects – 5 Most Effective Ways to Label Data
The dynamic nature of the property market, rapid onslaught of technology and cutting-edge competition are keeping property listing sites on their toes. Property data is the driving force for marketplaces, and how well real estate data aggregators capitalize it determines their market position and success.
A real estate marketplace connects realtors, brokers, and property managers to their customers. Its primary aim is to help users easily search through listings of properties, compare them, and buy or sell. Data aggregation companies collect, cleanse and manage property information to feed the marketplaces with data on mortgage, construction project leads, residential real estate, foreclosed properties, expired listings, and For Sale By Owner (FSBO) listings.
Availability and accessibility of reliable data is the key to user decisions made in the property lifecycle right from buying, financing, and constructing, to leasing and occupying the property. How well real estate marketplaces capitalize on this data determines their respective market positions and success. The depth, breadth and quality of the property data it hosts defines a marketplace’s user experience and consequent growth and profits.
Accurate real estate data aggregation empowers marketplaces to add value in terms of analytic features, easy and quick searches, neighborhood information, 360° view of the property and visual appeal to win an edge over the competition.
Real estate marketplaces keep gathering data for various reasons. However, over time, much of this data is discarded, lost or forgotten, or their formats become incompatible with new datasets and reporting requirements. Too often, the data is not shared and remains enclosed in silos. And lack of process documentation aggravates the situation. Inability to mark the method used in generating the data makes it unreliable and unusable.
All these factors influence the usefulness of collected data to a marketplace and its customers. If data is unreliable, then strategies become skewed, targets cannot be set, and reporting, benchmarking, and comparison for analysis becomes impossible.
Legacy systems prevent data aggregators from leveraging AI and ML tools and collect data in real time, at the scale and depth necessary in this hyper-competitive market. They increase the risks and errors associated with data aggregation.
Imagine the inherent risk in manually collecting, inputting, validating, and verifying property data of 3,100 counties, and approximately 230,000 cities, schools, and other jurisdictions.
With technology breakthroughs arriving back-to-back, it’s important for real estate data aggregators to rethink their adoption and use of tools. About 52% of companies once on the Fortune 500 list have become obsolete, for failing to keep up with the digital evolution, says HBR.
Real estate data providers are under immense pressure to keep their databases comprehensive, accurate and up to date. Forward-thinking aggregators are trying to manage decades of information like property valuation, asset management, customer listing, etc. But lack of skilled resources withholds them from incorporating machine learning into their data aggregation workflows.
According to KPMG only 5% of real estate aggregators have data management experts on board. And that’s why 80% of firms do not drive “most or all” of their decisions on data.
The data provision space is maturing with companies like CoStar, Real Capital, and Hitech BPO, who are making it possible for real estate marketplaces to quickly acquire relevant data at scale. They also help them in understanding what the customer wants and in enhancing user experience, while maintaining diligence and accuracy of information.
A few years ago, real estate customers had generalized expectations, which involved the money they would pay or receive, and property information before closing the deal. In case things didn’t go their way, they would tell friends or neighbors to not to use a certain realtor. The customer’s ability to offer feedback and impact a realtor’s brand image was limited.
Today, consumers want realtors to provide accurate information on neighborhood, pricing trends etc. that are useful in making critical buying/selling/rental/investment decisions.
Customers expect full transparency even in a simple $5 eCommerce transaction. So, imagine what they expect from a real estate transaction that involves their life savings. While inaccuracies or erroneous listings can push them easily to competitors, their online feedbacks can tarnish the brand image of any real estate marketplace. With the volatility of information that marketplaces must handle, meeting such expectations is a constant challenge.
Property data aggregation from multiple sources is one of the oldest and biggest challenges real estate database aggregators face. Today, most of the third-party data sources have their own schemas and taxonomies.
Real estate marketplaces must aggregate information on homes, commercial and other properties for sale and rent. They also should keep track of properties not currently on the market. And all these records need to be collected from a range of disparate sources. They include streams of county records, tax data, listings of homes for sale, listings of rental properties and mortgage information. Integrating all these while maintaining a homogenous and standardized database is a challenge.
Make comprehensive property data the driving force of your marketplace.
The real estate industry is on the verge of disruption. It has several opportunities to reduce friction between buyers and sellers – while increasing efficiency. But for all these, the data of property, owner, neighborhood etc. provided by real estate databases needs to be clean, accurate and voluminous.
Real estate marketplaces connect property buyers and sellers. A voluminous inventory optimized decently for search engines has the potential to increase the total number of visits and sales. The quantity and quality of the inventory aggregators maintain determine their ability to attract buyers.
We have worked with a real estate data aggregator from the US. Their business need was to collect 2 million property records from more than 1,000 small and medium MLS sites and third-party data sources. Accurate real estate data aggregation ultimately empowered the real estate portal to provide customers with home and apartment listings, shop for mortgages, and find information about 110 million homes across the US. It also helped them assess how their competitors were managing their property inventories across segments.
From small houses to big buildings, a real estate portal should have all kinds of data.
Data, aggregated accurately at scale, gives an idea about the size of the inventory. However, MLS sites also need information on how the properties are distributed among states and cities at the ZIP code level.
Real estate marketplaces compete on a national level in all 50 states across the US. In order to reach the top status in the real estate market they need insights into the inventory dynamics. A real estate portal may rank at the top when it comes to the total number of listings. But it might be ranked much lower in dynamic markets where a lot of real estate transactions take place. Only comprehensive, accurate and up-to-date data can fill that gap for the aggregator and the portal they are supplying data to.
A US-based real estate information services firm we worked with was looking for Zip-level property listing information to understand how it is distributed among competitors. Automated research and data aggregation, supervised by data professionals, was conducted to research zip codes for 270,000 properties from USPS websites to update records. Within a month, the inventory grew significantly, and in direct proportion to their competitors’ inventories.
Alike every industry, price makes or breaks a deal in the real estate industry. But how do buyers, sellers and investors ascertain what is the fair market price in a region?
Customers consistently monitor real estate listing sites for time-series data to benchmark the base price in a specific region. Thus, the success of listing sites depends heavily on robust real estate data collection. Because the same data is used to feed the portals. Hence, real estate data aggregators should:
All these would help a real estate marketplace to help buyers, sellers and investors understand property characteristics, and get an early view of critical property trends. It will also help them indicate real estate price fluctuations to make smarter business decisions. Brokers, investors, and every real estate industry stakeholder, can then fruitfully use the portals to access rich property data, convert customers and close deals.
Shelf velocity or how fast the inventory is being sold is a KPI real estate investors must track.
Data aggregators equipped to scrape near real-time data from across time zones can help a real estate marketplace to highlight interesting insights. They may include which market is moving into slow shelf velocity, or why liquidation of assets is difficult and slow in a particular market. This in turn would help real estate portals to warn buyers and investors accordingly.
Appealing property photos are vital for superior online buyer experience and growth of a marketplace. Attractive property photos play an essential role in converting prospects into buyers. Visual representations of property include:
Real estate marketplace can blend property information and enhanced property images, videos and walkthroughs to give a comprehensive view of all properties in the market, including current and past listings.
We post-processed 3,000+ HD quality photos every day for a real estate videography, photography, and 3D tours firm. They used edited property photos on their portal and digital catalogs. It improved their client’s brand credibility across Ohio, Indiana & North Carolina, and helped them capture greater market share.
All these years, the real estate industry was driven by speculation, educated guesses, and gut feeling. Use of legacy systems resulted in their inability to unlock the potential of decades of transaction records, valuations, asset management details, listing, and other datasets. They were not able to glean actionable insights to compete with confidence and survive concurrent market dynamics.
Its time real estate data aggregators start using scripts and schedule macros to automate activities like capturing property listings, demographic and socio-economic data, spatial data and even data from recently concluded real estate transactions. Analyzing accurately collected data can enable real estate portals to provide their customers with granular and accurate insights while searching and comparing properties.
Marketplaces can analyze data to develop valuation and property recommendation models to assist customers identify properties that best fit their budget, and those mortgages with high probability of over-valuation.
Advanced technology has successfully created an ecosystem for rapid advances, disruptions and has eliminated legacy methods and models used by real estate marketplaces. Numerous businesses and companies have been disrupted by technology, and the speed of these disruptions is significant. The Uber effect (car rental aggregation) has proved how quickly an industry can be changed by technology advances if adopted properly. Data aggregators who keep pace with technological advances and build risk mitigation into their business models will be able to position themselves in such a winning situation.
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