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Why build a price index?
Real estate market participants want to know what is happening to key market indicators. Amongst these indicators, prices are the most important. Construction, investment, sales and portfolio forecasts and decisions all rest on which way prices have moved, and by how much. The recent history of a real estate market’s performance is a collection of individual properties that have transacted. How can one convey the information within those transactions as accurately as possible? Some chartered surveyors still publish estimates of where they believe the market is based on the qualitative judgements of their researchers—many of these datasets do have the advantage of long time series. However, the most desirable and accurate is to combine all, or as much as possible, of the available information into one or more property indices using statistical techniques. Also, produce the resultant index as frequently as possible. But how? Price indexes are very different from one another, and what can be created depends on what data are collected for each individual transaction.
Simple Average Indices
The simplest index would be to add up the values of all the transactions for a given type of property in a particular period and divide by the number of properties sold to produce a median price. When transaction numbers are both large in number and relatively homogenous—that is, every property sold is very similar— then this simple method can produce reasonable results, even if they are often ‘lumpy’. But the more frequently the index is produced, the lumpier and less reliable is any individual data point. In some cases, there are insufficient transactions, especially at a local level, to produce a transactions index at all. In other cases, as CoreLogic in Australia points out, ‘when large unit developments hit the market all at the same time, the spike in volumes combined with the low turnover rate can result in average prices being skewed towards the new builds’. Data for off-plan sales can also take several months to feed through into official statistics.
Just as importantly, measuring prices without taking quality changes into account runs the risk that price change will be overestimated, one way or the other. Take offices in London as an example. Back in 1990, even Grade A office space did not have air-conditioning as standard. Now, no one could claim Grade A status without it. Modularity of office space is another comparative advantage of today’s offices. Residential property has not advanced so swiftly in qualitative terms— despite adaptions to green technology which may yet result in an entirely new generation of residential real estate—but there are clearly substantial qualitative variations between properties that are sold which can distort overall indices.
Most advanced real estate market indices have therefore developed beyond simple averages. But there are different ways to do so. What can be achieved depends significantly on data availability and capture.
Repeat Sales Indices
One way is to use repeat sales. Prices of individual properties that have transacted in the past are amalgamated to generate a view on how prices have changed. Some very interesting long-term price series have been generated using this method, for example for the Netherlands, which showed the way real estate prices have accelerated beyond general inflation dramatically in the last century. Whilst this might be possible in a market where the overwhelming majority of properties transacted are existing build, using it in the UAE would run the risk of failing to take into account the effect of new build. We are easily more than a decade away from being able to use such an index reliably anywhere in the Gulf.
Whilst repeat sales indices might be possible in a market where the overwhelming majority of properties transacted are existing build, using it in the UAE would run the risk of failing to take into account the effect of new build.
Hedonic house price indices
The second way is to take quality into account directly. The hedonic regression method uses ‘typical’ properties with specified characteristics to standardise transactions results. With the characteristics of each property standardised, the argument goes, all that can remain when observed prices change is a change in market price. Indices such as the Halifax house price index in the UK, which covers 300,000 transactions annually, and similar indexes produced by statistical offices in Sweden, Germany and other European countries use this method. CoreLogic in Australia has even extended the approach to produce a daily index which can form the basis for market trading on the Australian Stock Exchange.
In principle, this approach is ideal, but inevitably, as SQM Research noted in relation to the CoreLogic index in Australia, ‘for a hedonic index to truly work, it really needs a vast amount of data, particularly covering specific quality attributes of individual properties. By not covering the quality attributes adequately, the hedonic index itself risks providing incorrect, skewed results’. SQM rightly pointed to the increasing importance of land topography in determining house price values: in future, with 3D printing and other fast construction methods, the quality of the villa or apartment may well not matter as much as the desirability of the land in determining price. The Halifax index, for example, depends on the data provided by its customers, so if they are not completely reflective of the market, whether geographically, or in terms of the type of property they buy, then the index is likely to be skewed. The relative uniformity of the UAE residential property market by comparison to European markets does make a hedonic index likely to be more accurate: huge issues of stylistic and construction differences, large differences in build dates, and a multitude of localities and regions all militate against accuracy in European index results, which only the large volume of transactions serves to counteract.
What approach is right for the UAE?
In the UAE, there are market-leading lenders, each of which enjoys double-digit market share. But they are split between Dubai and Abu Dhabi, let alone the other emirates. More importantly, as yet, UAE lenders do not collect the kind of information required to construct a hedonic index.
The third way to build a reliable index is therefore to assemble all possible data series in the most sophisticated way possible. This is the approach taken by data intelligence platform Property Monitor (PM) in its indices. PM’s starting points are the most important available datasets: on sales registrations for new and completed properties from the Dubai Land Department, active listings, agreed sales, and valuations. The inclusion of valuations data is based on the established Sales Price Appraisal Ratio (SPAR) methodology which has been used for many years in New Zealand and which has recently been used as a benchmark index in reviewing alternative approaches by researchers in the Netherlands. Over time, a relationship between sales prices and appraisal prices can be discerned and forecast. This relationship is actually largely cyclical, the gap between them widening when property prices are falling and narrowing when they rise. The inclusion of recent appraisal data is especially valuable in a fast- moving market where lags in reporting transactions can result in market participants lacking confidence in published indices—not an issue in Dubai—but also to bolster data volumes in specific locations. There is also the added advantage of introducing implicit hedonic analysis thanks to the experience of valuers in recognising the advantages and disadvantages of specific properties. PM analysis then also equally importantly removes extreme values, outliers, and non-market transactions. Each index also includes a minimum transactions volume control to ensure that small numbers of sales do not skew the results. All the data that emerge from this cleansing process is then placed into categories by property type and location. The result is the Property Monitor Dynamic Price Index.
Property price indices have come a long way since chartered surveyors presented qualitative judgements and governments presented simple averages. Repeat sales, SPAR and hedonic regression methodologies have produced huge improvements in the accuracy of property price reporting and increased confidence in data reporting for market participants in general. However, each type of index has its strengths and weaknesses.
So, given the current state of available data for individual property transactions in Dubai, it is as well to utilise as many data sources as possible and use proven algorithms to combine them. The resultant indices are as good a combination as the suite of data series available within Dubai as can be achieved, and represent the basis for extension across the rest of the UAE.
 CoreLogic (2017) CoreLogic Home Value Hedonic Indices FAQs August 2017. Available at: https://www.corelogic.com.au/sites/default/files/2017-09/CL17_pdf Retrieved 27 January 2020
 Eichholtz, (1997) A Long Run House Price Index: The Herengracht Index, 1628–1973. Real Estate Economics 25(2), 175-192. https://doi.org/10.1111/1540-6229.00711
 Halifax Building Society (2020) Halifax House Price Available at: https://www.halifax.co.uk/media-centre/house-price-index/ Retrieved 27 January 2020
 Australian Stock Exchange (2020) Property Indices Available at: https://www.asx.com.au/asx/markets/propertyIndices.do Retrieved 27 January 2020
 Christopher, L. (2012) Weaknesses of a Hedonic Housing Price Index. SQM Research. Available at: https://sqmresearch.com.au/RPDailyIndexPiece.pdf Retrieved 27 January 2020
 Willenborg, L. and Scholtus, S. (2018) The SPAR index and some alternative house price indices. Statistics Netherland Research Paper. Available at: https://www.researchgate.net/publication/330619946_The_SPAR_index_and_some_alternative_house_price_indices Retrieved 27 January 2020.
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