When it comes to real estate data, hyper local is where it’s at. Servicers are now requiring real estate data providers to drill down past metropolitan statistical areas (MSAs) and even ZIP codes to access market trends on the neighborhood level, according to a new report from Integrated Asset Services (IAS). IAS provides default management and residential valuation services, which includes a monthly home price index. In a paper released Wednesday, IAS said that a new valuation method has emerged in the industry known as “intelligent granularity.” In developing this new method, data providers and servicers realized residential real estate markets are a “local phenomenon” influenced by factors on the neighborhood level. In other words, servicers are asking for trend lines for each neighborhood, and decision makers can use this new magnified perspective of the housing market to “gain a far better sense of what to do about properties and borrowers within each market,” according to the paper. These trend lines can be produced for a variety of data sources including census, socioeconomic, property and income information. “As a result, rather than depending on a hundred or so of the nation’s largest metro areas, any sort of valuation product can incorporate hundreds of thousands of very small areas around the country,” according to the paper. For REO, these new trend lines allow servicers to glimpse into the future of a local real estate market to anticipate effects on real estate values and shift marketing strategies for these properties to fit them. For instance, if trend values are on the decline in a particular area, servicers can elect to sell a property “as-is.” On the other hand, if trends are pointing up, servicers recommend repair work, anticipating a higher sales price in the future. “Because of the volatility of the market, it is increasingly important to have real-time, accurate information to better manage market plans for real estate,” according to the IAS paper. Write to Jon Prior.
Servicers Seek Granularity of Real Estate Data: IAS
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