Creating Open Data Standards for Real Estate, Appraisal, and Mortgage Banking

Article excerpt


The academic community can play a vital role in the development of open data standards within the real estate, appraisal, and mortgage banking professions as a key stakeholder group. What becomes clear from an examination of the future of data standards is the need for a strong academic/practitioner connection. It is also clear that the primary source for education and analytic cal tool sets must be the academic community, yet at present there is only minimal involvement by academics in the data standards effort.

The impact of open data standards in the real estate profession raises some critical questions for academic practitioners on how to meet the challenges that the continuing adoption of such standards pose for the profession.

An open data standard is one in which different software and systems work with one another independent of vendor, similar to the current functionality of web browsers and web servers.

In appealing to the academic community, several key questions must be examined and evaluated. The first question relates to the importance of standards to the real estate profession. Why should the academic community care about open data standards and what should the level of their participation be? Further, how does this open data standards effort differ from other technology-based standards efforts?

The valuation segment of real estate is the critical driver to understanding the marketplace, and how profound the changes could ultimately be. Lenders rely on collateral valuation to quantify risk and to hedge potential losses. If real estate has lagged other industries in its drive to integrate technological and data standardization, then valuation and standards within valuation space represent the final frontier.

Open data standards and technology innovation within the valuation and finance sectors of the real estate profession have reached a tipping point, and the drive towards wrenching the estimated $1 trillion in cost efficiencies will cause both chaos and create opportunities for those who can provide a meaningful solution to the industry. Industry estimates project that the overall drive towards data and process standardization will be so significant that the word ''tectonic'' has been suggested to describe the impact and scope.

Change has defined the status quo for virtually all business and organizational structures in recent years, and the real estate and appraisal industry are the focal points for changes that are ongoing within the financial services and informational service sectors of the economy.

Literature Review

Thomas Friedman, in ''The World is Flat'' (2005) describes the change in the status quo, and discusses how the world is being flattened by the drive towards technology and standards. These two drivers-open data standardization and technological innovation-are flattening the real estate world, and by extension, the valuation terrain. Far from self-serving hyperbole, it is clear that process and data standardization efforts will not only drive significant efficiencies into the market, they will eventually change the very nature of real estate analytics. For the first time, real estate practitioners who have been fond of saying that ''appraisal is an art, not a science'' will be in a position of having access to data and tools that can analyze that data at a level that will truly begin to resemble real estate econometrics.

It is generally recognized that greater efficiencies are one critical benefit of the adoption of standards (Cirincione, 2007). This is certainly true, but there are likely even greater structural changes on the horizon that might change the very paradigm of current analysis and analytics. It is clear that there are both practical and theoretical ramifications that must be considered and evaluated. The time may be right for a greater introspection of how standards may change the nature of the profession, and ultimately drive ever greater amounts of aggregated data that can be used for research and analysis in dramatically more efficient ways. …