Hear our experts’ take on the latest developments and trending topics
Who worries about data?
Both developers and investors, from homeowners to REITs, are critically dependent on accurate real estate data. Whilst actual purchase and sale prices are specific and recordable, both comparisons and forecasts are reliant on market indices. Active market participants will also need to make use of yield, construction cost, land price, and pipeline data, integrating their analysis with financial data on interest rates, loan volumes and macro conditions. Effective market research, the lynchpin of successful real estate development and investment, is impossible without reliable data: actions are effectively taken blind, and without it, developers and investors face significantly higher risks. Decisions about the extent of discounts or other incentives to offer are good examples of decisions that need to be data-driven; moving into new markets or deciding when to sell existing assets are two more. In each case, it must be conceded, accurate data of itself is insufficient, the analysis based on it must be dispassionate and influential as well. It is hard to believe that investors in some markets properly assessed the story already told by the data before they invested.
Accurate data are therefore the lifeblood of their key advisers, chartered surveyors. There is not only an inevitable reciprocity between market data and individual property valuation, but the provision of market data is an important element of the social licence to operate of chartered surveyors worldwide. The extent and quality of data provision is a hallmark of a significant market player in the real estate advisory market.
As the CTO of Property Monitor pointed out recently, banks also need access to transparent and accurate market data to correctly ascertain markers like Loan-to-Value (LTV) ratios and calculate risk to mitigate adverse outcomes, especially as IFRS9 guidelines are implemented across the UAE and the wider GCC.
Policymakers also need real estate market data. Their use for data predominantly dovetails with data on other indicators, such as employment, GDP and FDI and is directed at determining policy settings in areas such as tax, including service charges, fiscal and monetary incentives for market stimulus and restraint, regional and municipal boundary changes, and many other policy areas. In particular, concern over housing markets, both affordability and the market cycle, leads policymakers to evaluate price-to-rent, price-to-income ratios, which are critically dependent on accurate underlying market data for residential real estate. Policymakers also take a keen interest in the health of the real estate and construction industry, which requires data on housing and commercial property starts, completions and sales as well as LTV and market data, ideally based on transactions rather than valuations.
Eventually, as consultants have argued, micro-level ‘big data’ information about individual buildings will enable more sophisticated urban planning and facilities management and better modelling of customer needs, as well as integration with existing real estate market data, but this is some way off yet.
Gaining traction: characteristics of good real estate data
A number of consultants have advanced lists for data in general. Slightly different I suggest will be user-driven criteria for specifically real estate data, particularly in the specific meanings ascribable to each of my proposed TRACTOR headings, which I suggest are an advance on those proposed by the BIS in the past.
Conclusion: data matters
Four years ago, the World Economic Forum noted that although the real estate industry was globalising, data collection processes, definitions and reporting systems still vary widely between countries and sectors, as do definitions of widely used indicators such as yields, capitalisation rates, vacancy rates, effective rents, and prime and secondary grade assets.
The provision of widely accepted real estate data, especially that such as Property Monitor which conforms to TRACTOR criteria, has therefore been a vitally important step for Gulf real estate markets over the past decade. Participants in markets served by data of this quality are able to conduct market research, make investments, lend, and formulate public policy with the same degree of confidence as in Europe, Asia or other developed markets. The critical need now is for data of this quality to be made available throughout Gulf markets, to enable comparisons to be made between jurisdictions.
 Real Estate Business [Australia] (2018) Apartment price drop of 8% predicted for 2019. 6 November 2018. Available at: https://www.realestatebusiness.com.au/breaking-news/17930-apartment-price-drop-of-8-predicted-for-2019 Retrieved 6 June 2019.
 Accurate data critical to UAE banks mitigating risks of loan impairments under IFRS9 guidelines. Cavendish Maxwell, 6 May 2019. Available at: https://www.cavendishmaxwell.com/insights/opinion/accurate-data-critical-to-uae-banks-mitigating-risks-of-loan-impairments-under-ifrs9-guidelines Retrieved 6 June 2019.
 Kauko, T. (2018) Pricing and Sustainability of Urban Real Estate. London, Routledge.
 Mehrhoff, J. (2016) How should we measure residential property prices
to inform policy makers? Eighth IFC Conference on ‘Statistical implications of the new financial landscape’,
Basel, 8–9 September 2016. Bank for International Settlements. Available at: https://www.bis.org/ifc/publ/ifcb43_z.pdf Retrieved 6 June 2019.
 e.g. Deloitte (2018) Data is the new gold. The future of real estate service providers. Available at: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Public-Sector/gx-real-estate-data-new-gold.pdf Retrieved 6 June 2019.
 e.g. Ortega, D. (2017) Seven Characteristics That Define Quality Data. Blazent, 26 January 2017. https://www.blazent.com/seven-characteristics-define-quality-data/ Retrieved 6 June 2019.
 Boon, B. (2001) The availability and usefulness of real estate data in eastern Asia – a user’s perspective. Bank for International Settlements. Available at: https://www.bis.org/publ/bppdf/bispap21h.pdf Retrieved 6 June 2019.
 Australian Bureau of Statistics (2018) 6416.0 – Residential Property Price Indexes: Eight Capital Cities, Sep 2018. Explanatory Notes. 11 December 2018. Available at: https://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/6416.0Explanatory%20Notes1Sep%202018?OpenDocumentRetrieved 6 June 2019.
 World Economic Monitor (2015) Emerging Horizons in Real Estate. An Industry Initiative on Asset
Price Dynamics. Profiles, Prescriptions and Proposals. Available at: https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/newsandevents-site/workshops-site/Documents/DRees_Background-Paper_Emerging-Horizons-in-Real-Estate.pdf p.19. Retrieved 6 June 2019.
Stay up to date
|_ga||2 years||The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors.|
|_ga_34E12VSHW6||2 years||This cookie is installed by Google Analytics.|
|_gat_gtag_UA_66458947_1||1 minute||Set by Google to distinguish users.|
|_gat_UA-66458947-1||session||A variation of the _gat cookie set by Google Analytics and Google Tag Manager to allow website owners to track visitor behaviour and measure site performance. The pattern element in the name contains the unique identity number of the account or website it relates to.|
|_gid||1 day||Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously.|
|cookielawinfo-checkbox-statistics||1 year||This cookie is set by the GDPR Cookie Consent plugin to store the user consent for the cookies in the category "Statistics".|