The Residential Investor Jigsaw Part Two: Capital Values

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.