The Residential Investor Jigsaw Part Two: Capital Values
Julian Roche Chief Economist
How does Dubai compare? What is happening to residential property prices? Here are the data from Property Monitor to understand better. Probably the most useful column to use for comparison purposes is Transferred Sales, as this is the basis on which governments worldwide compile their own transactions-based indices. Both the Australian Bureau of Statistics and the UK’s Land Registry produce property data: the ABS generates an index directly, whereas in the case of the UK, average property prices are used. In all three cases, to make a comparison it is necessary to standardise, in this case using Q4 2016 as a base.
INDEX OF AVERAGE PROPERTY PRICES DUBAI Active Listings
Agreed Sales
Valuations
Transferred Sales
Q4 2016
100.0
100.0
100.0
100.0
Q4 2017
96.8
97.1
101.8
107.5
Q4 2018
92.1
90.2
87.0
98.1
Q4 2019
Average Annual Return USD
83.1
74.4
77.4
101.4
-6.0%
-9.4%
-8.2%
0.5%
The Dubai data are effectively already in USD, and it is very noticeable that transactions are the least affected by the market downturn. In terms of final result, Dubai at the end of 2019 ended up at more or less the same point as at the end of 2016, about 1% higher. The same could certainly not be said of the Australian market, which though now stabilising, has not enjoyed a bull run over the same period. What looks virtually like a level market in AUD in terms of annual returns looks rather less inviting when considered in USD. Investors in every Australian city would be worse off than those in Dubai if they measured their annual returns in USD. As to comparable final results, the data are compelling: only in Melbourne did investors keep their money, everywhere else they ended the three years worse off, in one case in double digits.
AUD/USD
Australia
Sydney
Melbourne
Brisbane
Adelaide
30 Sep 2016
0.743664
100.0
100.0
100.0
100.0
100.0
100.0
30 Sep 2017
0.766833
111.7
112.8
116.7
106.7
108.0
100.6
30 Sep 2018
0.747466
106.8
105.1
112.0
105.8
107.4
97.6
30 Sep 2019
0.695358
95.7
93.3
100.5
95.9
99.0
86.6
-1.5%
-2.3%
0.2%
-1.4%
-0.4%
-4.7%
100.0
100.0
100.0
100.0
100.0
100.0
Average Annual Return USD
30 Sep 2016
Perth
30 Sep 2017
108.3
109.4
113.2
103.5
104.8
97.6
30 Sep 2018
106.2
104.6
111.4
105.2
106.9
97.1
30 Sep 2019
102.3
99.8
107.5
102.5
105.8
92.7
0.8%
-0.1%
2.4%
0.8%
1.9%
-2.5%
Average Annual Return AUD
By contrast, UK investors have had a significantly better time, notwithstanding Brexit. London, the traditional UK destination for Arab real estate investors, has seen a deterioration in market sentiment and achieved prices, but secondary markets, notably Manchester and Edinburgh, have done relatively well. Their end results are up by double digits, even in USD, let alone sterling. It is certainly possible that disparities in value between capital cities and regional centres, which have been a pronounced feature of the European market for the past two decades, may now be reducing in importance. A similar process may start to take hold in the UAE, too, with prospects for economic development in Sharjah and the Northern Emirates.
30 Sep 2016
GBP/USD
UK
London
Birmingham
Bournemouth
1.355676
100.0
1100
100.0
100.0
100.0
100.0
Edinburgh Machester
30 Sep 2017
1.288611
99.4
96.0
102.1
99.0
102.5
105.1
30 Sep 2018
1.334801
105.0
98.6
112.0
107.2
114.6
113.0
30 Sep 2019
1.276933
102.8
94.8
108.3
100.1
116.1
111.7
Average Annual Return USD
0.9%
-1.8%
2.7%
0.0%
5.1%
3.8%
UK
London
Birmingham
Bournemouth
30 Sep 2016
100.0
100.0
100.0
100.0
100.0
100.0
30 Sep 2017
104.6
100.9
107.4
104.1
107.8
110.5
30 Sep 2018
106.6
100.2
113.8
108.9
116.4
114.7
30 Sep 2019
109.2
100.7
115.0
106.3
123.3
118.6
Average Annual Return GBP
3.0%
0.2%
4.8%
2.0%
7.2%
5.8%
Edinburgh Machester
The first level of capital values: overall market performance Policymakers love certainty as much as investors, and perhaps analysts respond to their wishes as much to their real needs. In the real estate context, empirical studies at the macro level would often seek ‘fundamentals’ by studying financial ratios comparing house prices – usually measured through an index – with macroeconomic supply and demand variables. The usual suspects were demographics, GDP, household income, interest rates, employment and income inequality, construction permits, and housing starts, gross fixed capital formation, mortgage conditions, and general inflation, usually measured by a Consumer Price Index. Household income was always at the core of the hypothesis. This in turn led to the view that given the slow response of supply—always characteristic of housing markets—and given that housing demand was highly responsive to income through the mortgage market, as income grew, price jumps that amplified the short-term volatility of the housing market would inevitably follow. Studies found that with employment dominating the mortgage market, house prices reacted quickly, and sometimes dramatically, to changes in real interest rates – after tax rates, for relevant jurisdictions. The house price boom of the last two decades owed much, no doubt, to prolonged low interest rates. Positive correlations were also found between construction permits and mortgage market conditions — and the rate of house price growth, which in turn owed much to GDP growth and low interest rates. As time progressed too, an increasing number of models began to incorporate ‘delta’ measures, such as perceived changes in trend productivity growth1, the rate of growth in household disposable income, gradual shifts in demographics, changes in the supply of land and to the cost of construction.
1
Kahn, J. (2009) What Drives Housing Prices?. NBER Research Paper. Available at: https://conference.nber.org/conferences/2009/efgs09/Kahn.pdf Retrieved 4 February
2020.
As has been observed, ‘The uncertainty about future prospects that follows periods of heightened volatility in housing prices tended, it was argued, to lead to a more cautious response of housing construction to shifts in demand because of the inherent irreversibility of this type of investment’.2 Mortgage market conditions, always regarded as important determining variables of house prices, were generally estimated by the ratio of domestic credit to the private sector as a percentage of GDP. On this view, house prices would be more sensitive to changes in the real interest rate when the rates were already low, and borrowers were extended. For example, Sydney and Melbourne during the middle years of the last decade, where the real interest rate was very low, and the price increases were commensurately higher. By the end of the last decade, policymakers had developed sophisticated macro models of house prices for both developed and developing countries. In the case of the latter it was even suggested that rapid price change would be ameliorated by better governance, institutional quality and economic freedom, for example, as measured by the Fraser Institute: the size of the government, the legal system, property rights, sound money, and freedom to trade internationally. These models were designed to predict house price bubbles, and the expression ‘overvalued’ was used to describe periods when the ratios exceeded historical averages. At the micro level, the traditional view3 known as ‘user cost’ was to estimate the costs of residential real estate, usually confined to an analysis of owner-occupiers only. It was suggested that the cost of owneroccupation consisted of opportunity cost, buying and selling costs of houses (such as registration fees and agent fees), running costs (such as repairs, service charges and insurance), the costs of the real interest rate, and depreciation. Together, this user cost ought to be compared to the ‘imputed rent’, or what it would have cost to rent an equivalent property, together with the foregone income that the owner would have received if the owner had invested the capital in an alternative investment. Theoretically at least, the comparison ought to take into account much more — differences in risk, any tax benefits from owner-occupancy such as tax relief from mortgage payments or special treatment of property in inheritance, property taxes such as capital gains tax, maintenance expenses and any anticipated capital gains from owning the home4. Derived from this was also a theory of house price bubbles: when priceto-rent ratios climbed beyond historical averages, the user cost was out of kilter with renting, and house prices would eventually fall. How distant both macro and micro analyses in this vein now seem. Bubbles could be identified, but not explained. If macro and micro factors worked so well, how did they ever happen in the first place? The key always lies in the future. Even early on, analysts recognised that expectations of future house price change would have to be a part of the ‘user cost’. Expectations could draw prices away from macro fundamentals ‘in the short term’.
2
Tsatsaronis, K. and Zhu, H. (2004) What drives housing price dynamics? BIS Quarterly Review. Available at: http://www.hkimr.org/uploads/conference_detail/775/con_
paper_0_211_zhu-haibin-paper.pdf Retrieved 4 February 2020 3
Fox, R. and Tulip, P. (2014) Is housing overvalued? Research discussion paper, Reserve Bank of Australia, RDP 2014-06, ISSN 1320-7729.
4
Himmelberg, C., Mayer, C. and Sinai, T. (2005) Assessing High House Prices: Bubbles, Fundamentals and Misperceptions Journal of Economic Perspectives 19(4), 67–92
As house prices in both developed and developing markets began to accelerate away from traditional fundamentals—nowhere more so than in the UAE—analysts scrambled to find new explanations. Perhaps the size of the distressed real estate inventory, the pace of price appreciation in an earlier period, or—more plausibly— the volume of sub-prime lending were all potential determinants of house price inflation. Ominously, the larger the increases in prices prior to the bust, it was said, the larger the house price declines that would inevitably follow5. Moreover, during periods of price correction residential property prices failed to adjust as quickly as the models had predicted, they were ‘sticky on the downside’. Analysts conjectured that this was because sellers were rooted in their original purchase prices and would rather accept other welfare losses than sell at below-par. The beginning of the takeover of the analytical world of real estate by behavioural economics had begun, and as house prices seemed to detach themselves from ‘fundamentals’ in ever more countries—even large cities in staid Germany6—a new way of thinking about the market was not only a theoretical desirability, but a practical necessity for policy-makers around the world, especially in the US7 8. In truth, the answer had already been given. Robert Schiller as long ago as 2000 had pointed to the fact that ‘irrational’ price expectations for residential real estate9 indeed exist. He argued that Americans had turned into a ‘nation of speculators’, explained by money illusion, false intuition and storytelling. In succeeding studies10, self-fulfilling beliefs, irrational expectations and sentiments concerning expected future price trends were all held responsible for both the heightened price volatility by comparison to previous decades and the higher average residential real estate prices that major markets have experienced in recent years11. Where America led, the rest of the world rapidly followed, the UAE certainly being no exception. We know where the irrational exuberance led; what remains is like the smile on the Cheshire cat in the old story — the cat has gone, but the smile remains. So, what does now make people buy houses? It’s complicated, everyone agrees. Studies seem to suggest though that the reasons will continue to differ between cultures and across economies. In developed countries like the Czech Republic, for example, it seems as if economic factors are just not that important, even for first-time buyers. More formally, ‘the assumption of economic theory that ownership and renting are risk-adjusted economic substitutes may be significantly constrained’12 and social norms may play a decisive role. On the other hand, recent analysis of Indian buyers suggests that financial factors—price range and availability of home loans—are still very much the most important factors in decision-making13. Half-way in between, market research conducted on the house buying behaviour of young people in Turkey concluded that both macro and micro factors played a role, but that greater wealth, predictably enough, pushed decision-making towards value-based rather than financial choices14. It does look as if the more developed the market, the less the traditional metrics for market value will hold.
5
Follain, J.R. and Giertz, S.H. (2013) Preventing House Price Bubbles. Lessons from the 2006–2012 Bust. Policy Focus Report. Lincoln Institute of Land Policy. Available at:
https://www.lincolninst.edu/sites/default/files/pubfiles/preventing-house-price-bubbles-full.pdf Retrieved 4 February 2020 6 Kajuth
F., Knetsch T.A.,and Pinkwart N. (2013) Assessing house prices in Germany: evidence from an estimated stock-flow model using regional data. Discussion paper
Deutsche Bundesbank 46/2013 7 Follain, J.R. 8 Mayer,
and Giertz, S.H. (2013) ibid.
C. (2011) Housing Bubbles: A Survey. Annual Review of Economics 3, 559-577. Available [in draft] at: https://www0.gsb.columbia.edu/mygsb/faculty/research/
pubfiles/5636/MayerHousingBubbles.pdf Retrieved 4 February 2020 9 Schiller, 10 Quite 11
R. J. (2000) Irrational Exuberance. Princeton, NJ, Princeton University Press.
possibly all real estate, but at that point, most investors could really only enter the residential market.
See e.g. Case, K., Cotter, J. & Gabriel, S. (2011) Housing risk and return: Evidence from a housing asset-pricing model, The Journal of Portfolio Management, 37(5), pp.
89–109. 12 Lux,M. 13
Gibas, P., Boumová, I. and Hájek, M. (2017) Housing market behaviour of first-time buyers in the Czech Republic. Housing Studies 32(4), 517–539., p.518
Kumar, Y. and Khandelwal, U. (2018) Factors Affecting Buying Behaviour in the Purchase of Residential Property: A Factor Analysis Approach. Journal on Customer
Relations; New Delhi 6(2), 27-32. 14
Liu, Y. & Li, Z. (2018) Determinants of Housing Purchase Decision: An Empirical Study of the High Education Cohort in Urban China, Journal of Asian Architecture and
Building Engineering, 17:2, 299-305, DOI: 10.3130/jaabe.17.299Available at: https://www.tandfonline.com/doi/pdf/10.3130/jaabe.17.299 Retrieved 4 February 2020
The second level of capital values: locality The eventual aim of this series is to analyse total returns, so the two tables below show the capital values for the Dubai localities for which the yields were presented in The Residential Investor Jigsaw Part One: Yields over the same period. Three key facts stand out. Firstly, the performance of capital values over the past three years exhibits a wide distribution. Secondly, the lack of correlation between capital value and yield performance is quite evident. Thirdly, however, the distribution is wider among the highest yielding apartments than the lowest. In the former case, the worst performer, IMPZ, showed a downturn of 36%, whilst the best performing, City Walk, showed a 1% increase. In the latter case, the worst performer, International City, was down 31%, whilst the best, Palm Jumeirah, was also down, albeit by only 12%. In addition, the dispersion of returns is less for the lowest yielding apartments. A graph shows the point:
APARTMENT PRICES 2016 AND 2019 2,000
Price per sq ft (AED)
1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 City Walk
Palm Jumeirah
Al Furjan
Downtown Burj Khalifa
Business Bay
AED/sq ft Sep 2016
Discovery Gardens
Jumeirah Lakes Towers
International City
Dubai Residence Complex
AED/sq ft Sep 2019
IMPZ
CAPITAL VALUES FOR THE LOWEST YIELDING DISTRICTS OF DUBAI (AED/ SQ FT) Sep 2016
Sep 2017
Sep 2018
Sep 2019
786
726
618
556
Dubai Marina
1,409
1,473
1,380
1,200
DIFC
1,871
1,811
1,743
1,503
702
680
571
485
Palm Jumeirah
1,466
1,539
1,446
1,285
Downtown Burj Khalifa
1,886
1,910
1,807
1,619
866
862
791
695
Culture Village
1,620
1,590
1,414
1,217
Jumeirah Beach Residence
1,374
1,360
1,358
1,077
Business Bay
1,346
1,416
1,328
1,176
Location Dubai Residence Complex (Sky Court Towers)
International City
Remraam
CAPITAL VALUES FOR THE HIGHEST YIELDING DISTRICTS OF DUBAI (AED/ SQ FT) Sep 2016
Sep 2017
Sep 2018
Sep 2019
894
911
812
727
1,380
1,390
1,184
1,046
910
872
789
580
Zabeel
1,489
1,475
1,440
1,168
City Walk
1,571
1,822
1,689
1,594
Jumeirah Lakes Towers
1,185
1,171
1,023
885
910
952
953
899
1,071
925
911
829
Dubai Sports City
922
896
820
707
Discovery Gardens
831
855
696
609
Location Motor City Emirates Living IMPZ
Al Furjan (Avenue Residence) Jumeirah Village Triangle (Imperial Residence)
VILLA PRICES IN DUBAI (AED/ SQ FT) Sep 2016
Sep 2017
Sep 2018
Sep 2019
950
919
879
679
Arabian Ranches
1,146
1,057
913
845
Arabian Ranches 2
1,150
1,057
913
845
Dubai Silicon Oasis (Cedre Villas)
841
808
752
678
Falcon City of Wonders
851
826
782
660
Motor City (Green Community)
999
987
950
852
Jumeirah Golf Estates Villas
1,128
1,209
1,044
997
Jumeirah Islands
1,264
1,228
1,125
1,015
Jumeirah Park
1,091
1,039
933
869
Jumeirah Village Triangle
1,066
905
811
695
Mohammed Bin Rashid City (Polo Townhouse)
1,447
1,037
1,212
1,326
Mudon
908
797
830
791
Reem
789
824
781
695
The Villa
821
808
734
647
Dubai Investment Park
829
751
632
586
Dubai Sports City (Victory Heights)
1,175
1,061
982
894
Emirates Living
1,147
1,089
970
939
Location
Al Furjan Villas
From the chart above for villas, the average decline in value is once again 20%, and the variation in price performance is intermediate between the two apartment samples. So, there is evidently a wide variation in performance between localities. But what exactly causes the disparity? For those who are seeking explanations, the game is now on. One example, aside from local oversupply or the attractions of specific, ever-larger individual properties, is the role of foreign buyers. Clearly, they are important in key international markets, so analysts are now chasing down individual districts in London, for example, and correlating their performance with political events in the ‘home country’. Not surprisingly, this work has confirmed that yes, following heightened risk in Greece, for example, there were relatively higher prices in specific areas of London where Greeks tend to buy. Generalising the approach, the analysis did find economically large, statistically significant, and robust effects of foreign risk on house prices in locations in which the shares of foreign-origin London residents were above average. ‘Our empirical results provide a careful analysis of whether foreign capital flows have affected real estate prices and price volatility, especially in global cities such as London and New York’.15 And, no doubt, the Dubai and probably also the Abu Dhabi of the future, if not already today.
15
Badarinza, C. and Ramadorai T., Home away from home? Foreign demand and London house prices Journal of Financial Economics 130(3), 532–555
The third level of capital values: hedonic analysis In the past, the general view16 17 was that residential property attributes could be classified into three classes, namely, location attributes (access to social and economic facilities), structural traits (floor area, floor height, age etc.) and neighbourhood characteristics (the 2000s view that if Starbucks or Tesco set up in your neighbourhood, it was good for prices). Hedonic models all recognised the essentially local nature of real estate prices, especially residential real estate, and so strived to establish realistic localities with meaningful geographic boundaries that would make sense as independent variables into regression equations. Even more difficult has been to establish adequate quantified variables for construction quality, even though anecdotally there is no doubt of its contribution to individual property value in every jurisdiction, along with branding and fit-out. Transport has been another major factor in hedonic analysis. Whilst some wealthy families invest in luxurious apartments in city centres because of proximate access to amenities and public transportation (e.g., New York City, London, Melbourne), some studies have shown increasing scepticism regarding the effect of proximity to light rail, even in countries such as the US. One study in China18 developed a model of a monocentric city which is oil-using and potentially oil-exporting. The model and empirical findings supported the prediction that home price changes in the inner areas of such cities positively correlated with the price of oil. Do we believe though, that in future, the price of oil will feed through petrol prices at the pump to a sharper bid-rent curve—more expensive properties closer to the centre. Almost certainly not: but results have also continued to indicate that even if jobs are no longer the allpowerful determinants of property market value, and therefore commuting will gradually lose its pride of place in hedonic price analysis, proximity to the CBD remains enticing and a strong determinant of higher value. Studies have also showed that the age of property was a critical negative variable in many markets, whilst the floor area, garage and parking space, number of bedrooms and bathrooms were all important positive variables. In a hedonic variable study of Dubai, the total area of the house (in sq m) is found to be the only variable that appeared to have effects that did not vary along the entire conditional price distribution. The study found that although the positive and significant effect of total house area was consistent with prior expectations, buyers of higher-priced homes appear not to price house areas differently from buyers of lower-priced or medium-priced houses. This kind of result can be of tremendous importance to marketing departments, for obvious reasons. Sometimes variables were idiosyncratic. A sea view always matters, especially for more expensive property, but it mattered more than anything in Sydney, for example, whilst in parts of London, age was a positive variable, not a negative one. In other cities, Lagos for example, it was obvious that the availability of neighbourhood security significantly influenced property values, as it does in Johannesburg and US cities19. House type has also always been a factor, with unexpected results occurring with regard to the relative value of villas and apartments. Whilst academic and political studies have been conducted regarding housing preferences20, it is valuers, especially chartered surveyors, who have built up impressive hedonic price databases based on their experience to assist them in accurate valuations.
16
e.g. Wong, K., So, A.T. and Hung, Y. (2002), Neural network vs hedonic price model: appraisal ofhigh-density condominiums. In Wang, K. and Wolverton, M. L. Real Estate
Valuation Theory, 181-198. 17
Wen, H.Z., Jia, S.H. and Guo, X.Y. (2005), Hedonic price analysis of urban housing: an empiricalresearch on Hangzhou, China., Journal of Zhejiang University Science,
6A(8), 907-914. 18 Larson, W. 19 Abidoye, 20
D. and Zhao, W. (2017), Oil Prices and Urban Housing Demand. Real Estate Economics.
R.B. and Chan, A.P.C. (2016) Critical determinants of residential property value: professionals’ perspective Journal of Facilities Management 14(3), 283-300
Kaya, S. K., Ozdemir, Y., & Dal, M. (2019). Home-buying behaviour model of Generation Y in Turkey. International Journal of Housing Markets and Analysis [no issue
number]. Available at: https://www.researchgate.net/publication/336020661_Home-buying_behaviour_model_of_Generation_Y_in_Turkey Retrieved 4 February 2020
Conclusion There is little doubt now that internationally—not only in the UAE, but in economies that were once dominated by manufacturing, such as the UK, the real estate market and its associated largely private sector debt are now envisaged as the central drivers of economic prosperity, a ‘real estate-driven regime’.21 The UAE is moving into that regime as the built environment inevitably progressively dominates the volume of construction. The era when the UAE could still be considered a developing market with prices that exhibited low correlation with global asset indices is therefore also ending: ‘the UAE, Australian and US real estate markets’ beta increased and hence became more integrated with the world stock market after the real estate crisis, with the UAE registering the largest increase’22. Yet, residential property prices in developed markets, including the UAE, continue to demonstrate both wide variations and considerable oscillation even over a relatively short timeframe. Localities demonstrate considerably different performance. The decision to buy remains a multi-level one, influenced increasingly by changing cultural norms as by rational economic choice. No doubt it is difficult as a result, but there can be no doubt that for investors and the government alike, accurate forecasting of residential real estate prices, at both the macro and locality level, is the central task for investors of every stripe. In this context, it is rather welcome news for most analysts that the disadvantages of very complex models of housing markets probably outweigh the advantages. What matters, studies show 23, is picking the right model. For that, investors need to turn to their advisers.
21
Hoffman, A. and Aalbers, M.B. (2019) A finance- and real estate-driven regime in the United Kingdom
Geoforum 100, 89–100, p.89. 22 Hatemi-J, A. Roca E. and Al-Shayeb, A. (2014) How integrated are real estate markets with the world market? Evidence from case-wise bootstrap
analysis. Economic Modelling 37 (2014) 137–142, p.141 23
For a review of which models work best where, see Boitan, I.A. (2016) Residential property prices’ modeling: evidence from selected European
countries Journal of European Real Estate Research 9(3) 273-285
DUBAI
ABU DHABI
SHARJAH
MUSCAT
2205 Marina Plaza
605 West Tower, Abu Dhabi Mall
1801 Sarh Al Emarat Tower
Villa 836, Way 3012
Dubai Marina
Tourist Club Area
Buhaira Corniche Street
Al Sarooj
P.O. Box 118624
P.O. Box 126609
P.O. Box 38583
P.O. Box 3438
Dubai
Abu Dhabi
Sharjah
Muscat
United Arab Emirates
United Arab Emirates
United Arab Emirates
Sultanate of Oman
T: +971 4 453 9525
T: +971 2 448 4677
T: +971 6 715 0444
T: +968 24 694 150
Disclaimer: The information and analysis contained in this report is based on information from a variety of sources generally regarded to be reliable, and assumptions which are considered reasonable, and which was current at the time of undertaking market research, but no representation is made as to their accuracy or completeness. We reserve the right to vary our methodology and to edit or discontinue the indices at any time, for regulatory or other reasons. The report and analysis do not purport to represent a formal valuation of any property interest and must not be construed as such. Such analyses, including forward-looking statements are opinions and estimates only, and are based on a wide range of variables which may not be capable of being determined with accuracy. Variation in any one of these indicators can have a material impact on the analysis and we draw your attention to this. Cavendish Maxwell and Property Monitor do not accept any liability in negligence or otherwise for any loss or damage suffered by any party resulting from reliance on this report.