Where AVMs work best
AVMs work best with widely traded, homogenous assets. Their performance degrades with increasingly heterogenous assets or assets that are thinly traded.
For real estate assets, it is unsurprising that residential property – particularly classes of property that are traded reasonably frequently and have similar characteristics – has led the adoption of AVMs in mature markets. As an asset class, residential property has good data availability for lending, mass appraisal and consumer facing valuations.
Even within residential property, there is a risk in applying AVMs where properties don’t fulfil the basic criteria of homogeneity and a sufficiently liquid and transparent market.
Consumers may use some form of AVM to get an idea of value when considering entering a property transaction. Alternatively, a broker may employ an AVM to calculate value when marketing a property. For both re-mortgage and origination purposes, a lender may use an AVM to support their <strong>underwriting<strong> process in addition to other considerations such as affordability. In all these cases, consumers are owed clarity and transparency regarding how these values are being calculated.
In the Instant Buyer (or ‘iBuyer’) business model, companies purchase residential properties directly from private sellers and eventually re-sell them. With the emergence of this model, there is a strong need for consumer protection and education to ensure fairness on prices offered versus resale values. However, with the US operator Zillow withdrawing from this market in November 2021, citing market volatility, the long-term sustainability of this business model is unclear.
It is also worth highlighting that institutional build-to-rent, or ‘multi-family’, to use the US term, is increasingly being valued using AVMs in many markets.
The nature of effective AVMs in commercial real estate (CRE) and the data sources required are very different from residential property. This is because the various classes of CRE represent much more heterogenous and thinly traded assets than residential. However, there are many instances of the development and application of AVMs in markets where assets are being traded with similar characteristics and a sufficiently deep enough data set of property attributes and market data. Some of these AVMs are producing capital valuations on the assumption of vacant possession, with others focusing on forecasting market rents.
Another active role for automation is in the creation of various market indices around the movement and forecasting of market metrics such as rents, capital values, and yields at sector and subsector level. At portfolio level, we are already seeing the implementation of AVM approaches for CRE, albeit with the caveat that the efficacy and accuracy will tend to degrade as you drill down into segments, subsegments, and individual assets themselves.
One of the oldest phrases in computing is ‘garbage in, garbage out’. While the same principle applies to the data underpinning a valuation produced without the use of an AVM, the efficacy of any AVM is underpinned by the data used to develop and operate it.
At a simple level, we need data that is of a high quality since no model can overcome a lack of data or data that is erroneous. But what does it mean in practice?
Quality in the context of AVMs means considering the following factors, with an emphasis on transparency around every aspect of the data sources being used: recency; availability; security, privacy, ownership, and ethics; provenance and lineage; assurance; consistency; collection methodology; scale and range.
Another consideration on data sources for AVMs, and indeed for non-automated valuations, is the increasing variety of data being used as part of the valuation process. Some of these data points can be seen as proxies for more traditional attributes, such as using crime rates to assess the attractiveness or otherwise of the location and region, Tripadvisor/Airbnb ratings, air quality or broadband availability. In the case of CRE, there is increasing use of location type characteristics, such as local amenities and the quality of transport links, in a way that is already well embedded for residential properties.
With the rise of environmental, social, and governance (ESG) measures as a driver of value, both AVMs and non-automated valuations need increasing access to various data points around energy performance certification and accreditation schemes. These include, for example, Energy Performance Certificates (EPCs) and the Building Research Establishment's Environmental Assessment Method (BREEAM). Ideally this data should include actual energy performance in addition to certificates of theoretical performance. The need for ESG data is becoming critical across all forms of valuation to firmly establish the correlation and causation of links between ESG and value for both residential and CRE properties.
Caution needs to be taken when adding additional data sources during the use of an AVM, since the effect on the models needs to be measured and the models recalibrated against external reference points.