The vital importance of market data for real estate market stakeholders

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[1].

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[2].


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[3], 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[4].

Eventually, as consultants have argued[5], 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[6]. 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[7].

  • Timely. Data must reflect users’ needs. If current demand is the issue, then 2016’s take-up rate is not sufficiently timely. Significantly, what constitutes timely data for real estate is undergoing a metamorphosis: real time data may not be that far away, although a paucity of actual transactions and the heterogeneity of real estate will mean that although valuations may end up continuous, transactions data cannot be so, even with much faster and possibly more frequent transactions enabled by the blockchain in decades to come.
  • Reliable. Under this heading are the following aspects of real estate data
    • Definitions must change as little as possible over time and between cities and regions, so that we can compare like with like. Changes in quality, however, do make it impossible never to make any changes, which means that for example in Australia, residential price data from before 2004 is not wholly consistent with data thereafter, necessitating the publication of two data series[8]. From a bank perspective, a change in a freehold property’s value between the time of purchase and a potential foreclosure is a complex combination of land appreciation, physical deterioration of the structure and local demand and supply.
    • There must be market confidence in the data provider, their integrity, solvency and ability to reach all stakeholders in the market.
  • A As real estate data are principally supplied by the private sector, questions of affordability and ease of access are both relevant for users. Delivery mechanisms must be well supported from an IT standpoint, including where relevant privacy and other legal conformity.
  • C Under this heading I place at least three components.
    • Market segment. Real estate has seen growing interest in different components of the investment universe. Residential real estate is now frequently divided into apartments and villas, commercial now also includes not only hotels but property types hitherto more usually associated with government or multinationals, such as schools, hospitals, airports and refineries.
    • Geographical Area. This is a constant problem for real estate data that is shared by few other datasets. The boundaries of geographical areas can have a very significant impact on data results. Municipalities are engaged in regular boundary changes, which renders reliance on public sector zoning problematic. Any boundary change must be flagged up clearly and well in advance by the data provider.
    • Length of time series. Although the longer the better, as an approximate guide, a forecast can be made for X periods of time ahead with reasonable confidence if 2X of history is available.
  • T Again because of its source, the methodology used by a real estate data provider must be publicly available in order to generate required market confidence. Transparency also assists in maintaining the credibility of the data provider, especially at times when market conditions are changing rapidly, and where there are many users whose profitability and even survival may depend on the consequences.
  • O Data from any provider must be mutually compatible, provided in a suitable format for users. Definitions of property quality, and where provided ratio information such as yields, must be clearly presented and as far as possible compatible with best international practice.
  • R As data will be used for the range of purposes described above, it is essential that publication is regular and predictable. The frequency with which data are issued in the real estate industry has been a source of regret and concern for financial professionals for decades: transactions are often insufficient to permit reliable even monthly indices.


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[9].

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.

 

[1] 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.

[2] Abeidat, M. (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/blog/accurate-data-critical-uae-banks-mitigating-risks-loan-impairments-ifrs9-guidelines/ Retrieved 6 June 2019.

[3] Kauko, T. (2018) Pricing and Sustainability of Urban Real Estate. London, Routledge.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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?OpenDocument Retrieved 6 June 2019.

[9] 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.

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