The volume, complexity, and availability of data across disparate sources make it necessary for companies to ensure that the quality of data or accuracy of their data is best. Its effect on the real estate industry is no different. With help of accurate data:

  • Real estate developers & investors can better find properties with higher potential
  • Real estate agents or realtors can serve their clients more efficiently
  • Property buyers and sellers get an experience that is free of clutter and redundancy

Earlier real estate players used to rely on traditional data points like lot size, pricing, area population, nearby gymnasiums, etc. Big time increase in volumes of data and the use of analytics has added new and non-traditional data points that play a critical role in making informed property decisions. Leading players like Zillow, Trulia, and Redfin have already shown the real estate industry the true power of using a blend of traditional and non-traditional data to fuel the growth of their business.

Non-traditional data sets include levels of noise pollution, no. of grocery stores nearby, ratings of nearby restaurants, etc. There certain machine learning algorithms capable of analyzing more than 200 non-traditional data points to give futuristic insights about property.

This increasing awareness of the value of data sets has made real estate players realize the importance of maintaining a comprehensive and accurate database of properties.

Why accurate data collection is critical for real estate players:

1. Financial risk reduction

Mitigating financial risks is amongst the prime requirements of companies functioning in the real estate sector. With the help of predictive analytics, companies can analyze multiple parameters of property and make informed decisions before investing their time and money in it. Though a predictive analysis model works only as good as the accuracy of the data collected.

2. Enhanced customer service and engagement

Accurate customer data can help real estate companies analyze the customer profile and understand the needs of one or a group of customers. Hence, accurate data can help real estate companies can provide better property deals and suggestions based on the type of customer.

3. Brisk property evaluations

Property evaluations are complicated as it needs loads of information of the property like title deeds, ownership agreements, location, locality, amenities to name a few. Collecting this information accurately and feeding it to your analytic tools can help you perfectly evaluate a property and that too at a really fast pace.

4. Improved decision making

Accurate data collection helps the customers make an informed decision about the property they are willing to buy. This helps solve all the hassles which occur between real estate companies and customers in the property buying process.

5. Creating feasible marketing strategies

Understanding the need of the customer is key to selling him a property. Collecting accurate information of your customer like age, job profile, yearly income, interests, etc. are keys to make a successful deal. Big data and machine learning can process this customer information and provide you with customer profiles that match the property you are willing to sell.

To attain all these benefits, it is imperative for real estate companies to use accurately collect data and gain a competitive advantage. So now the question stands!

How to maintain accuracy while collecting real estate data?

Data in real estate comes from a wide range of sources like open public records, multiple listing sites (MLS), geolocation APIs, and physical property deed documents, etc. Here is how you can be sure the data collected by you is accurate:

1. Accurate property document processing

Unfortunately even today, title and deed documents are in physical form. Extracting information from these documents is usually done manually, allowing errors such as address mismatches, zip code errors, listing mismatches, etc. to creep in the system. Errors may occur due to handwritten annotations, missing information, or misinterpretation of information by the data entry operator.

Every year, data entry errors cost real estate companies millions of dollars in time and resources. This can be avoided by implementing a standardized and automated data capture process to ensure data accuracy.

2. Cleansing data collected from Multi Listing Sites (MLS)

Every real estate player – agencies, brokers, or sellers depend on Multi Listing Services (MLS) data for property valuation, risk mitigation & other analytical insights. As of 2019, there are 640 MLSs in the US, which means an average of 12 MLSs per state. , The information stored in MLS is filled by member real estate professionals, which means there are a million (1,374,774) people performing data entry in MLS, which has no standard schema, data validation, or data verification process. Hence, the following steps should be taken before using data from MLS:

  1. Addresses should be considered as the ‘unique key’ and checked for duplication
  2. MLS listings have a lot of missing information that needs to be filled in.
  3. Put together the disparate formats of MLSs into a single standard format
  4. Cross-referencing this information with other sources

3. Checking government and public records

Government and public property records are a great place to check for anomalies in your data. The accessibility of such information varies from state to state. Some of them provide information online while others don’t. Despite these problems, it is critical to check your data against such resources to fix errors and improve the accuracy of your real estate data.

4. Choosing the right APIs

The importance of non-traditional data has brought into the picture multiple API providers who provide information on various aspects that affect property value like geolocation, nearby restaurant ratings, nearby amenities, area pollution level, etc. Choosing the right API provider to collate this data will affect the accuracy of your data and insight tremendously.

5. Outsource real estate data management

There are outsourcing service providers who have years of experience in real estate data collection. They have the appropriate resource, expertise, and knowledge to source all the above-mentioned information. If that’s not enough, they can also guide you on how to create a real estate data management system that can help you gain market insights, competitive advantage, and make profitable growth.

Conclusion:

In the current scenario, real estate players have understood that traditional, as well as non-traditional property data, can provide them significant insights with respect to pricing, customer service, risk management, and creating marketing strategies. To achieve this, they need clean and accurate data for which implementing extensive data collection techniques is all but necessary.

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