GeoPhy built a proprietary automated valuation platform for commercial real estate by using data science and machine learning techniques. They gathered and linked thousands of data sources using data fusion and natural language processing. Models were trained using this extensive data to evaluate properties and provide instant valuations with a median error rate of 5.85%, outperforming traditional appraisals. This platform allows lenders and investors to access more accurate property valuations instantly instead of waiting weeks for an appraisal.

1. How GeoPhy built a proprietary automated valuation platform for the commercial real estate
sector.
Valueprop: Real estate is the largest asset class in the world. With its own global industry classification
(GICs) it makes up, on average, 5.1% of any institutional real estate portfolio. However, determining
the value of commercial real estate remains elusive, with a workforce of 74,000 appraisers in the U.S.
alone, still manually assessing the value of assets, sometimes worth billions of dollars.
Appraisals are typically based on the capitalization of the net income of an asset, using a yield (or cap
rate) that has been inferred from neighboring transactions. However, transactions of comparable
properties are never truly comparable, neither in time, nor in building characteristics, and appraisals are
typically anchored on previous valuations or the previous transaction price of a building. The result is
that property appraisals typically lag the market and provide “smoothed” approximations of true market
values, with values that are artificially low in bull markets and high in bear markets.
Technology= Using data science and supervised machine learning to optimize the unprecedented
volume of data now available in the sector, we worked at GeoPhy exposing decisive features that drive
fast, consistent, trusted real estate values. Working with some of the largest lenders and investors in
real estate across the globe, their technology now offers a direct alternative for the traditional appraiser
workflow. Instead of waiting for weeks to get a valuation that is only marginally accurate, their clients
have access to instant and more accurate valuations.
Guiding the team, we developed a practical application of big data in combination with sophisticated
modelling techniques, to develop an automated, machine-based valuation model for the commercial
real estate sector.
How we did it:
Data Fusion:
Big Data and Hadoop: Internally developed data parsing algorithms have been designed to capture and
structure information out of the most common forms of unstructured data, appraisal reports, lease
contracts, transactions and mortgage deeds.
Semantic Data & Ontology: GeoPhy’s data management platform has been designed and developed
around a proprietary Real Estate Ontology – a knowledge graph for the real estate domain that allows
GeoPhy to rapidly structure and link heterogeneous data sources. This allows GeoPhy to gather and
link thousands of different data sources into one consistent overview of the global real estate market.
Data enrichment:
 Location: Understanding local markets and being able to convey timely information about them can
make the difference between the boom or bust of real estate investments. GeoPhy leverages and
enhances today’s unprecedented amount of demographic, socio- economic and hyperlocal data (e.g.
schools, stores and access to public transportation) to identify growth opportunities that keep our clients
ahead of the curve in today’s competitive real estate market.
 Geospatial reach: Using detailed networks for roads, transit and walking, GeoPhy determines the
actual reach in travel minutes from any location on earth. This allows them to compare and benchmark
a location from hyperlocal (what bars and restaurants are within 5 minutes of my location) to high level
macro (how many people live within 8 hours driving).
 REIT Portfolio Data: GeoPhy tracks the portfolio composition of every major listed property company
in the world. By combining that dataset of over 600 companies with their unique enrichment layers,
they can create portfolio benchmarking on metrics such as sustainability, portfolio quality and portfolio
risk.

2. How we built a valuation platform
 Supervised Machine Learning: Algorithms can outperform human experts in fields where data is rich
and deep, allowing for continuous feedback loops and learning models that can adjust and refine over
time. Commercial Real Estate used to lack a comprehensive and sufficiently deep database until
GeoPhy developed its proprietary data management platform. Using reinforcement learning their
models become more accurate every day.
 Real Time: Getting an appraisal used to be a process that took weeks, from initial assignment
to final report. GeoPhy eliminates that lag in information flow with instant results from their AVM
(automated valuation model), which is dynamically updated with new market transactions each day.
 Reliable: GeoPhy measures the accuracy of their models by comparing valuations to the flow of sales
in prices in the market as transactions close. Their current model accuracy is a MAPE (Median Absolute
Prediction Error) of 5.85%. This means that the median predicted value is just 5.85% from the actual
transaction price – twice as accurate as traditional appraisals for commercial real estate.
Advantage=
For underwriting and refinancing purposes, automated valuation models can provide an instant
indication of property value, which saves both portfolio investors and lenders, as well as those
interested in a single property, significant time and resources. This is especially beneficial on the
lending side, where the debt service coverage ratio is a leading indicator, with the LTV as an important,
but secondary input in the underwriting process.
Automated approvals provide banks, insurance companies, pension funds, and other institutional
investors and lenders with an instant, accurate revaluation of the assets on the balance sheet, obviating
the need for an annual (or quarterly) expensive and lengthy revaluation process which regulators
increasingly require. Such instant assessment of the market value of the book is especially useful in
times of market volatility.