How COVID-19 Can Accelerate Public-Private Sector Collaboration with Property Data

Back in September Georg Chmiel wrote about how property portals are becoming the de-facto source of property information for private individuals and companies and in doing so are overtaking governments. But what happens when the governments themselves that have fallen behind in the data game need to look to fresh, clean and accurate property data to stabilise their domestic housing markets in a time of uncertainty and economic turmoil? 

Jesús Armand Calejero, Head of Data at Spanish PropTech firm urbanData Analytics, believes there is scope for collaboration between governments and PropTech companies from the private sector which can lead to better data behind government reports and in turn to greater predictability and stability in the housing market...

Both the public administration and the private sector agree that collaboration between the two could be the key to the recovery of the Spanish housing market. The PropTech community has the big-data expertise to aid the government in analysing market behaviour and predicting tendencies and in doing so make the market that much more predictable, stable and sustainable.

A recently released Spanish government plan for the recuperation, transformation and resilience of the property market, which addresses the impact of Covid-19 and how to harness aid set aside by the EU, states that “public-private collaboration will be key to the execution of various projects”.

At urbanData Analytics we believe that this has long been the case and that an attitude of collaboration would be particularly beneficial for the Spanish property sector, especially in the face of the challenges faced in reactivating the market after the pandemic. The subject was brought up at one of the Sima Pro debates (the top Spanish B2B real estate meet-up). Both the public administration and the private companies involved have agreed to go down this road and act as a catalyst to the economic recovery of the country. 

A recent government report on the state of the rental sector in Spain serves as a stark example of just how necessary public-private cooperation in the sector is. Although the report’s publication in June was ostensibly good news for all stakeholders in the market and was presented as “a tool to guarantee the transparency and knowledge of the evolution of the rental market and to apply public policy that increases the supply of accessible housing”, the data used was from 2015-2018. The report is due to be updated at the end of 2020 with data from 2019, but is that enough to understand an ever-changing rental market?

The main objective of PropTech companies such as urbanData Analytics since its foundation in 2013 has been to bring transparency to the market. uDA offers information in real-time for its users (private individuals, investment funds, banks and developers) through big-data, a robust data acquisition process and machine learning. The agility with which we can add official data sources, process information and the top data science team we have at our disposal lets uDA generate 190 financial indicators and a ‘micro-localized’ analysis of any given property: Price change, median price, gross profitability, transaction volumes etc. We also generate our own KPIs such as liquidity, dynamism and risk and relative affordability.

All of these data points provide a complete vision of the market and help predict tendencies. Reaching an agreement to work with the public sector would put all the tools to understand such an essential sector into the public domain and help everyone make safer decisions. 

Covid-19 has changed a great many things, and governments have spared no expense in trying to find solutions that mitigate the economic impact of the pandemic. The IMF estimates that international public debt will reach record highs of more than 101% of global GDP in 2020-2021. Clearly, governments across the world will be desperate to reduce this figure as much as possible. 

There is a huge amount of experience and capacity in the private sector to assess and administrate assets and here we have another opportunity for collaboration between private and public sectors: the adaptation and utilization of publicly owned real estate assets to generate a profit.

Starting point for market transparency in Spain: AVMs

uDA’s automated valuation models use a minimum of 14 characteristics of each property. These variables define the asset from a structural point of view. However, the differentiating factor of our automated valuation models is the inclusion of machine learning techniques and the addition of information around square footage, construction date and number of bedrooms and bathrooms which all help to understand more about each asset.

Over the last few months, the data team has been working on the incorporation of third-party data sources that record seismic activity, temperature and access to services such as public transport and schools, indicators which directly affect the price of a property and add more complete information to it as an asset.

For example, according to a recent study on flood risk in property valuations, the price variation for properties located on flood plains varies between -7% to +1% depending mainly on the risk factor and time since the last flood. By the same token, understanding the hours of sun that a property receives in winter can help in understanding the impact of having central heating or electric heating and, if we incorporate an analysis of these types of costs, the information available to the pricing model is that much richer.

One of the chief challenges in the Spanish real estate industry in the long term is leveraging automated valuation models at scale. As a product, AVMs are relatively new in Spain and have a long way to go. In other countries such as The United States they have already demonstrated their value in removing subjectivity in the process of real estate valuations which in turn favours market stability and avoids big costly fluctuations.

By limiting the margin for error in valuations we narrow the gap between asking price and sale price and in doing so foster a more sustainable dynamic in our housing market which can see us through times of crisis.