The US Monetary Conditions and Dubai’s
Real Estate Market: Twist or Tango?
Ahmed Shoukry Rashad
1,2
and Mahmoud A. Farghally
3
1
Anwar Gargash Diplomatic Academy, Abu Dhabi, UAE
2
Economic Research Forum, Giza, Egypt
3
Ministry of Human Resources and Emiratisation, Policies and Research Department, Dubai,
UAE
JEL D14, G21, R21, R31, R28, R38
Abstract
The global monetary conditions are important driver of the real estate sector performance. Few
studies have looked the impact of the global monetary conditions on the real estate market in
the GCC region. This study fills this gap by exploring the impact of the US financial conditions
on Dubai’s real estate, as the UAE pegs its currency to the US dollar. We examine two main
channels through which the US monetary policy affects the real estate market in Dubai: (1) the
cost of serving mortgage; (2) the exchange rate channel. For this purpose, the study collected
unique longitudinal data on the volume of the monthly transactions of the residential properties
and performs a panel-data analysis using within-variation models. The changes in the interest
rate policy in the US are determined by the domestic inflation in the US, thereby, representing
an exogenous change to the UAE. Our results are robust to different specifications and suggest
that a strong negative correlation between the interest rate in the US and the housing sector
demand in Dubai. Fiscal policy measures can be taken to mitigate the tighter financial
conditions in case of policy misalignment.
I. Introduction
The global financial conditions are an important determinant of the real estate market (Deghi,
Natalucci et al. 2022). High lending rates have an immediate impact on mortgage lenders and
impacts the demand for new homes. Recently, the property markets worldwide have been
exposed to new pressures after the supply chain crisis that is associated with the Covid-19
pandemic lockdown and the economic consequences of the Russian invasion, which prompted
central banks to hike interest rates at the fastest pace in decades to combat inflation. Tighter
financial conditions weaken the demand for properties by making it less affordable for
mortgage buyers to finance home purchases or refinance existing loans driving down house
prices.
Dubai is an international real estate market, thanks to its laws that allow foreigners to
own properties, a stable currency that is pegged to the US dollar, the emirate’s advanced
infrastructure facilities, and the city’s attractions. The construction and the real estate sector
together constitute about 13.5% of Dubai's economy, with the real estate sector accounting for
6.9 percent and the construction sector accounting for 6.6 percent of Dubai’s GDP in 2021. In
fact, the non-tradable sector usually plays a vital role in the oil economies and the natural-
resource dependent ones, a phenomenon known as the Dutch disease.
The UAE currency has been pegged to the US dollar since November 1997, at AED
3.67 to the dollar, which has shaped the UAE monetary policy by aligning the UAE Central
Bank interest rate with the US Federal Funds’ target rate. The peg to the dollar, which is the
main currency used in international reserves and transactions, reduces foreign exchange-related
risks and uncertainty as much of the Gulf countries' revenue comes from oil that is priced
internationally in the US dollar. A stable exchange rate is also critical for foreign trade-
dependent countries. For example, Dubai’s total imports and exports reached 213 percent of
Dubai’s GDP in the year 2018. But on the other hand, a fixed exchange rate ties policymakers’
hands as it imports monetary policy from the US. But if macroeconomic fluctuations are not in
harmony, it is possible that a dollar peg may not be supportive and generates the risk of policy
misalignment when economic cycles are out of step. For instance, the country (the US) that is
in control of monetary policy might be hiking interest rates to curb domestic inflation at the
same time the other country with the pegged rate is going into recession. This has been evident
in the economic conditions that follow the Covid-19 pandemic, as the US economy quickly
recovered from the pandemic and became overheated with high inflation rates and tight labour
market thanks to the strong economic stimulus policies. While the Gulf economies were in the
process of recovering from the effects of the pandemic and with moderate inflation rates. For
example, the inflation rate in the US in 2021 is around 4.7% (above the Fed target) and in the
UAE were about 0.2% (International Monetary Fund 2022). Unlike Twist, Tango requires the
partners to strictly maintain harmony and synchronize their steps, the fixed exchange rate
regime works at best when the two countries involved have similar macroeconomic
fluctuations.
The recent waves of financial tightening in the US have affected the property markets
worldwide and increased the risk of macro instability. Albeit the importance of the real estate
sector in the Gulf economies and Dubai in particular, the impact of the US monetary conditions
on the housing market in the Gulf region and Dubai has been understudied. Evaluating the
impact of the US monetary conditions on the UAE housing market has paramount importance
from the macroprudential perspective. The study fills the gap by evaluating the effect of the
US interest-rate policy on the performance of the housing market in Dubai using longitudinal
data.
The following graphical representation illustrates the two main monetary policy
transmission channels through which the monetary conditions in the US can affect the property
demand in Dubai. The premise is that changes in the US interest rate will be followed by
changes in the short-term interest rate in the UAE, which in turn affects the long-term interest
rate and the mortgage rates increasing the cost of borrowing or refinancing for mortgage
buyers. Mortgages play an important role in Dubai’s economy, where the value of mortgages
represents about half of the total value of the real estate transactions in Dubai. Additionally,
higher borrowing costs could also motivate investors to shift to other types of liquid
investments with a higher return.
The second channel is the changes in the Fed rate affects the value of the US dollar and
automatically the Dirham value that in turn affect the property prices in Dubai. For example, a
stronger US Dollar raises the property relative prices in Dubai for foreigners with non-dollar
incomes. Dubai is a cosmopolitan city in which foreigners represent more than 90% of its
population. The city striving to attract real estate investors from abroad, thereby the impact of
the exchange rate channel on the real estate market is expected to be exceptionally large. For
example, Figure 2 shows the top investors in the Dubai Real Estate market by nationality in
2018. The figure clearly shows the significant number of foreign investments in the real estate
market, which might be sensitive to fluctuations in the exchange rate. Additionally, tighter
monetary conditions have an indirect effect on the real estate market by decelerating economic
activity, weakening the aggregate demand and the real estate investments.
Using unique monthly panel data on the real estate transactions as well as macro
variables, the study evaluates the effect of the US monetary conditions on the real estate market
using variation models. This study has important policy implications for policymakers. Firstly,
the study quantifies the impact of the Fed monetary tightening on the real estate demand in
Dubai. Secondly, the study discusses alternative policies that might be considered to ease
pressure on the sector and counterbalance the impact of monetary tightening in case of policy
misalignment. To our knowledge, no previous study has focused on the relationship between
monetary conditions and real estate demand activity in the UAE. We are not aware of any study
that has applied a panel-data analysis and used recent data to empirically study the effect of the
US monetary conditions on the real estate market in Dubai
Figure 1: Monetary Policy Transmission Mechanism and the UAE Real Estate Market
Source: Dubai Economic Report 2019
The study is organized as follows the next section reviews the literature on monetary
policy and the housing market, Section III provides an overview of Dubai’s real estate market
recently. Section IV discusses the data and methodology, and Section V presents the findings
and results while Section VI is the conclusion and main findings of the study.
0
2
4
6
8
10
12
UAE India UK KSA Pakistan China Jordan Egypt Canada Iraq France
Figure 2: Real Estate Investments in Dubai by Nationality in
2018 (AED Billion)
II. Literature Review
The economic literature is abundant with studies on the effect of changes on monetary
conditions and the real estate market performance. For example, Alhodiry, Rjoub et al. (2021)
tested the impact of the external shocks, oil prices, and the U.S interest rate on Turkey’s real
estate market by using three techniques of co-integration tests and concluded that there is a
significant spillover influence of the U.S. interest rates on Turkey’s real estate market through
oil prices and domestic interest rates. DeFusco and Paciorek (2017) used data from 2.7 million
mortgages in the US market to provide an understanding of the response of mortgage demand
to the changes in public financial policy using the elasticity of the interest rate of mortgage
demand in the US market and examined the power of the interest rate as a tool on mortgage
demand, (DeFusco and Paciorek 2017) findings illustrated the importance of interest rates as
one of the tools that can be used to control mortgage demand beside many other factors such
as mortgage terms, down payment and the choice between fixed or adjustable interest rate and
estimated that a 1 percentage point increase in the rate on a 30-year fixed-rate mortgage reduces
first mortgage demand by between 2 and 3 percent, and one-third of the response is driven by
borrowers who take out second mortgages while leaving their total mortgage balance
unchanged. Similarly, Lo (2017) examined the micro-elasticity of mortgage demand to interest
rate trying to set the base for more understanding of the relationship between mortgage demand
and interest rate as a key driver of mortgage demand, Lo (2017) developed a novel
methodology to measure the demand elasticity of purchase mortgages to interest rates. Lo
(2017) estimates suggest that individuals who are buying a new home are sensitive to interest
rates both on the extensive (choosing whether to get a mortgage) and intensive (the size of the
mortgage) margins.
Duca, Muellbauer et al. (2016) looked at how certain reforms can affect mortgage
demand using a loan to value (LTV) ratio and interest rate as explanatory variables, and their
simulation suggested that the interest rate may have a relatively smaller effect while changing
in mortgage policies such as LTV may have a larger effect. Bhutta and Keys (2016) studied
the role of interest rate in equity extraction decisions during 1999-2010 to understand the
mechanism of how the monetary policy using interest rate is transmitted into the economy and
found that lower interest rates were strongly associated with greater equity extraction during
the housing crisis in 2008, and this association is shown to be robust to a variety of
measurement techniques and specification checks, (Bhutta and Keys 2016) also suggested that
other variables are playing a key role in the response to interest rates such as house prices and
individual’s credit scores. Bhutta and Keys (2016) examine to what extent the US federal
reserve bank can use the interest rate to stimulate the economy by examining the effect of the
interest rate on mortgage demand and argue that the effect of interest rates on the current
mortgage is not enough and should be combined with the previous interest rates since a
significant share of mortgage on a fixed term, and concluded that due to the nature of the effect
of the interest rate on mortgage demand, using interest rate can be lead to a limited effect on
mortgage demand, especially during recessions.
Brueckner (1994) explores the determinants of mortgage and mortgage size decision
using both the return of investment and interest rate as key factors of mortgage demand size in
the US and concluded that mortgage size is a factor of both interest rate and return of
investment. Alm and Follain (1987) and Vickery (2007) examined the determinants of the
household demand for an adjustable-rate mortgage versus a fixed-rate mortgage and indicated
that as expected the demand for an adjustable-rate mortgage (ARM) is more sensitive to the
volatility in interest rate.
At the regional level, few studies have investigated the determinants of real estate
market performance in the GCC. Ziaei (2014) studied the effect of the monetary interest rate
on the GCC countries' aggregate demand component including real estate demand and found
that interest rate plays a main role in all the GDP components of the GCC countries except for
Kuwait and the most effective of interest rate was seen on investment component. Hepşen and
Vatansever (2012) main purpose is to investigate whether there is a long-term relationship
between macroeconomic indicators and the property price index in Dubai using monthly data
from 2003 to 2010. To identify a long-term equilibrium between the property price index and
macroeconomic indicators and the results of the empirical analyses show that there is a long-
term positive equilibrium relationship Dubai Residential Property Price Index (DRPPI) volume
of total direct foreign trade and gold price and a negative long-run relationship between DRPPI
and number of completed residential units. In addition, there is a significant positive relation
between DRPPI and the DRPPI (lagged).
Yousef (2019) investigates the determinants of capital structure in the UK and the GCC
countries and found that GCC real estate companies rely less on debt capital than their
counterparts in the UK which could be explained that UK companies typically face a lower
cost of debt and thus have increased access to debt capital in the market, and the main
differences between the GCC and UK samples of real estate firms appear concerning tangibility
and growth. A significant positive impact of tangibility on book and market short-term debt
was observed. Elsheshtawy (2019) studied the major transformation and transitions of the real
estate sector in the city of Dubai and connected these transitions with the sector speculations,
these transitions include the different city urban planning starting from the 1950s as well as the
main structural challenges facing the real estate sector in Dubai and concluded that the majority
of the real estate demand in the city are speculators.
Dahan (2018) aimed at estimating the demand of the Dubai real estate sector expressed
by demand for buildings and calculating the city real estate demand elasticity as a step to
forecast the demand in the coming years using price, income, population and interest rate, and
the paper concluded that both interest rate and population have no significant effect of buildings
demand, however, the demand for buildings is growing and expected to almost double between
2018 and 2020. Renaud (2012) examines the economic and real estate sector situations during
the period from 2003 and 2008 to determine the main reasons for the real estate bubbles in this
period study and tried to shed the light on the main characteristics and contributor variables of
this bubble.
Al‐Malkawi and Pillai (2013) analyzed the performance of real estate and construction
companies in the United Arab Emirates (UAE) during the pre (2006‐2007) and post (2008‐
2009) global financial crisis periods and found a negative impact on the business cycle on the
performance of real estate companies in the UAE. Woertz (2008) examined the effects of the
global 2008 financial crises on the GCC countries including the real estate sectors of these
countries throughout the effect of the financial options on the mega and luxury real estate
projects which were hit the most by the crisis and find out that most of the effect of the financial
crisis was migrated to the real estate sector. Abdelgalil (2005) examines the relationship
between the Dubai real estate sector and the financial sector at the macroeconomic and
microeconomic levels and found that the real estate sector plays a main role in both macro and
microeconomic levels of the Dubai financial sector and real estate prices is the main connector
of this relationship throughout direct and indirect effects of the real estate prices on the banking
sector lending policies.
III. Dubai Real Estate Overview
Dubai real estate sector is a key driver of the economy of Dubai, and since 2001 the Dubai real
estate cycle was a reflection of the Emirate economic cycle and witnessed two peaks, two
recessions and two growth points as illustrated in figure 3, the period from 2001 to end of 2008
witnessed the highest expansion ever in the real estate market in line with the city expansion
plan and the freehold law, however, in late 2008 and mid-2009 the financial crisis had a huge
impact on the sector performance and the sector performance sharply declined through 2011
reaching -28.46 percent declining in the constructions and real estate activities growth rate;
with the government reforms and the formation of escrow accounts system, the sector started
a recovery phase reached the peak in 2014 with another hit in oil prices fall and the U.S – China
trade war the sector starting a soft landing until 2020 which the outbreak of COVID19 and its
implications persist. However, by mid-2020 the UAE and Dubai government intervention
injecting AED 7.1 billion into Dubai's economy succeeded to mitigate the sector and started a
new phase of potential growth in line with Expo 2020 and the city's new urban plan 2040
expansion.
Source: Calculated by the Authors using DLD data.
Source: Dubai Statistics Center (DCS)
In line with the city expansion, the financing options available to the real estate
investors and homeowners were presented with mortgage Law No. 14 of 2008 where lenders
-30
-20
-10
0
10
20
30
40
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Q1
2022
Figure 4: Constructions and Real Estate Activities Growth
Rate
Real estate activities growth rate Construction growth rate
Growth
Decline
Recover
y
Soft Declining
Potential
growth
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Number of Transactions
Years
Figure 3: Dubai Real Estate Sector Cycle
(2021 –
2023)
(2015 –
2018)
(2012 –
2014)
(2009 –
2011)
(2004 – 2008)
have specified that the mortgage shall be a bank, or a financing company or institution duly
authorized and registered with the Central Bank of the United Arab Emirates for practicing
property financing activities in the Emirate, and in 2013, the UAE central bank issued the
regulations regarding mortgage loans to ensure that all banks are providing mortgage services
according to the best practices and set the maximum loan-to-value ratio (LTV) to a maximum
of 80% for UAE nationals and a maximum of 75% for non-nationals.
In March 2020 consistent with the government plan to mitigate real estate investment
the CBUAE reviewed the LTV rations and increased the LTV ration for first home buyers by
5 percent for both UAE nationals and non-nationals. These policies were reflected in the real
estate sector indicators as shown in the aggregated data from the Dubai Land Department for
the daily registered transactions.
Yearly transaction volumes in figure 5 show an average above 61 thousand transactions
registered yearly in the period from 2008 to 2021, with the highest volume of transactions
registered in 2009 with more than 100k transactions in line with the city expansion in freehold
ownership from nearly 33 percent in 1998 to 52.2 percent in 2009 with an approximate of 209
nationalities investing In the real estate sector in Dubai in 2019 with more than 80 percent of
foreigners real estate investors(DEpartment 2020), and the formation of RERA escrow account.
Despite the unprecedented restraints caused by COVID-19, the Dubai real estate sector
registered the second highest number of transactions in a single year in 2021 with more than
83 thousand transactions that is associated with the government stimulus packages including
the recent changes in the loan-to-value ratio by the UAE Central Bank and the expansionary
monetary policy. Analysis of the real estate transactions by transaction type reveals that despite
the low rate of mortgage compared to several sales transactions, the year 2021 registered the
highest number of mortgages ever registered in the Dubai real estate sector with 19,525
mortgages. Moreover, data for registered mortgages in freehold vs non-freehold areas shows
that the share of the mortgages in the freehold areas started to increase respectively in 2007 to
reach 4217 and 5469 in the freehold and non-freehold areas respectively representing 43.5
percent of the total mortgages in freehold areas.
The number of mortgages in freehold areas grew in the following years of 2007 to reach
its highest share ever in 2009 with 12825 mortgages registered in the freehold areas
representing 50.9 percent of total mortgages and since then the share of mortgages in the
freehold areas maintain a level, not below the 48 percent and increased to 48.9 percent in 2021
and 49.8 percent in 2022 in line with the recent changes in LTV.
Source: Dubai Land Department (DLD)
IV. Data and Method
We collected data from several official open sources. Data on the number of monthly sold
houses are collected from Dubai Land Department (DLD) covering the period from the year
2014 to July 2022. Our data on home transactions is broken down into two groups based on the
type of buyers, mortgage buyers, and non-mortgage buyers. Such a breakdown would allow us
to apply panel data methods and eliminate any unobserved time-constant effects. Data on the
US interest rate are collected from the FRED database (see https://fred.stlouisfed.org/ for
details). To control for the effect of home prices on housing demand, we use the Dubai House
Price Index produced by DLD. The Dubai House Price Index is a monthly index that is based
on hedonic price methodology, and goes back to the year 2011. (see
https://dubailand.gov.ae/en/open-data/residential-properties-price-index-rppi/#/ for more
details). House renting is a substitute good for house owners and might be an important
determinant for the housing demand. The DLD produces a monthly index on the performance
of the residential rental market. Similarly, the index uses the hedonic imputation method and
the data goes back to the year 2012 (see https://dubailand.gov.ae/en/open-data/residential-
rental-performance-index/#/ for more details). Although Dubai is the most diversified economy
in the GCC region, the oil price is an important determinant of the external demand; therefore,
we collected data on the global price of Brent Crude from the Fred database. As the population
size is not estimated on monthly basis and is an important factor for housing units demand,
monthly numbers on active mobile subscriptions in the UAE are collected from the
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
Figure 5: Real estate transaction volume by transaction type - Annual
Mortgages Sales
Telecommunication and Digital Government Regulatory Authority (visit: https://tdra.gov.ae/
for details) as a proxy. Our data can be described as long panel data since period T is far larger
than the number of groups N. We have monthly data on two groups of properties (mortgage
financed and non-mortgage financed) covering 86 months per group starting from January
2015. Thus, both a generalized least square model as well a fixed effect linear model with an
AR(1) disturbance can be adopted to take into account the large T. The following equation
represents our housing unit demand model as a function of the US interest rate, price of houses
in Dubai, rent prices in Dubai, global oil prices, etc:
(1)
Our dependent variable ∆ log(House
it
) is the approximate annual growth rate in property
transactions in Dubai at month t for the property group i. ∆interest is the annual change in the
US interest rate (interest
t
-interest
t-12
). We expect the US interest rate coefficient β
1
to have a
negative effect on the annual growth of properties, as the interest rate goes up and the cost of
borrowing increases the demand for properties falls. ∆Brent is the annual change in the global
Brent price (Brent
t
-Brent
t-12
). The expected sign for β
2
is positive. As the oil revenue increases,
we expect a spill over in the housing market. ∆price is the annual change in the Dubai House
Price Index (index
t
-index
t-12
), and we expect it to have a negative effect, reflecting the typical
inverse relationship between the quantity demand and the price. ∆Rent is the change in the
Dubai rent index compared to a year ago. If renting a property is a substitute good for property
ownership, we expect a positive coefficient for the variable rent. The variable Covid is a
dummy variable for the Covid lockdown period in Dubai. We expect β
5
to carry a negative
sign, as the Covid restrictions and the uncertainty around the pandemic dampen the aggregate
demand. As the Covid lockdown period witnessed a sharp drop in property deals, the months
of March 2021 and April 2021 saw a steep growth in property sales. Thus, we include the
dummy variable Base to account for the base-year effect. ∆log (mobile) is the approximate
annual growth rate in the number of active mobile subscriptions. This variable serves as a proxy
for population growth as well as economic activity, which is a fundamental driver of housing
demand. As the economy grows, the demand for foreign labour increases, and that is reflected
in population numbers. Therefore, we expect β
7
to be a positive coefficient. β
8
is the coefficient
for the interaction term Mortgage×∆interest, where Mortgage is a dummy variable for the
mortgage financed properties. The model control for the fixed effect and eliminates serial
correlation and heteroscedasticity by using the variables in difference form.
V. Results
Descriptive Results
Figure 6 presents the association between the US discount rate and real estate transactions in
Dubai over the period between January 2014 to July 2022. The inverse association between the
US discount rate and housing demand is quite obvious in the case of non-mortgage buyers, as
the discount rate went up after 2016, the property units sold went down and vice versa. For the
mortgage buyers, the inverse correlation is less visually obvious and can be further checked in
the regression models.
Figure 6: Real Estate Sector and the US Interest Rate
Table 1 provides panel summary statistics that decompose the total variation in the variables
into within variation over time and between variation across groups. It shows also the mean,
the minimum and maximum, and the number of observations for each group. The dependent
variable ∆log(house) varies over time as well as across the two groups and the degree of
variation is quite close. For all other variables, there is variation over time and zero between
variations, as they are group-invariant regressors. The fixed effect estimation methods are
solely looking at the within variation, ignoring the variation across groups.
Table 1: Panel Summary Statistics: Within and Between Variation
Variable
Mean
Std. Dev.
Min
Max
Observations
∆log(house)
overall
0.02
0.39
-1.08
1.83
N = 182
between
0.03
0.00
0.05
n = 2
within
0.39
-1.06
1.85
T = 91
US Discount
rate
overall
1.25
0.90
0.25
3.00
N = 206
between
0.00
1.25
1.25
n = 2
within
0.90
0.25
3.00
T = 103
∆interest
overall
-0.07
1.02
-2.75
1.00
N = 172
between
0.00
-0.07
-0.07
n = 2
within
1.02
-2.75
1.00
T = 86
log(house)
overall
7.20
0.43
5.73
8.24
N = 206
between
0.37
6.94
7.46
n = 2
within
0.35
5.69
8.15
T = 103
Oil Prices
overall
65.64
21.35
26.80
117.70
N = 206
between
0.00
65.64
65.64
n = 2
within
21.35
26.80
117.70
T = 103
log(mobiles)
overall
16.72
0.07
16.61
16.83
N = 198
between
0.00
16.72
16.72
n = 2
within
0.07
16.61
16.83
T = 99
Price Index
overall
107.01
6.78
94.90
118.70
N = 206
between
0.00
107.01
107.01
n = 2
within
6.78
94.90
118.70
T = 103
Rent Index
overall
101.39
11.43
79.50
117.80
N = 206
between
0.00
101.39
101.39
n = 2
within
11.43
79.50
117.80
T = 103
Results from within Estimation Models
To evaluate the effect of the monetary policy on the housing market in Dubai, we employ a
fixed effect linear model with AR(1) disturbance and use different within-estimation models
(generalized least squares (gls) and pooled OLS with AR(1) term and standard errors that
allows correlation between groups) as a robustness check. Table 2 provides a comparison of
various estimation models. Model 1 is a fixed effect linear model with an AR(1) disturbance.
It accounts for the fixed effect and eliminates the effect of the AR(1) error. Model 2 is the GLS
model assuming an AR(1) error and correlation across the two groups of properties. Model 3
is the pooled OLS with AR(1) term.
As expected, the coefficient of the annual change in interest rate has the expected
negative sign. Model (1) suggests an increase in the interest rate (year over year) by one
percentage point will cause about 17.7%[exp (-0.195)-1=-0.177] fall in the overall real estate
deals. This is economically large and the coefficient is highly significant at a 1% level of
significance. This finding remains robust across the other two models, the generalized least
square assuming AR(1) error and correlation across the two groups as well as the pooled OLS
model that assumes AR(1), and the interest rate coefficient’s value remains almost unchanged
to different estimation methods. The variable Rent has a positive sign, which may suggest that
as rents increase, properties purchase increase too. This is a reasonable finding, as renting
properties can be a substitute good for owning homes. However, the effect of rent on real estate
transactions is not economically large (about 1%). Although the annual change in oil price has
the expected positive sign, the coefficient is not statistically significant across different
specifications. The annual growth in the number of active mobile subscriptions in the UAE is
not significantly correlated with the movements in the real estate market. In line with the law
of demand, there is a negative association between the annual change in the Dubai House Price
Index and the growth in properties purchased. Model (1) suggests an increase in the Price Index
by 1 unit will cause about a 2% fall in purchased properties. The coefficient is significant at a
5% level of significance for model 1 and a 10% level of significance for model 3. The
interaction term (Mortgage×∆interest) was not statistically significant suggesting that the effect
of the interest rate change is the same for both mortgage buyers and non-mortgage buyers. The
two variables Covid and Base both control for the Covid lockdown and restrictions. The Covid
dummy accounts for the sharp drop in purchased properties after the strict lockdown in Dubai.
The three months lockdown causes about a 53% fall in purchased properties compared to the
previous period. The base dummy variable accounts for the sharp growth in purchased
properties, as we are comparing the growth in purchased properties to a low base period (the
Covid lockdown period). Thus, the period from March 2021 to June 2021 witnessed a sharp
annual growth of 185%. Our fixed effect model explains well the variation in the dependent
variable, and the adjusted R
square equals 0.55.
Table 2: Regression Results
(1)
FE-AR
(2)
GLSAR
(3)
OLSCORR
-0.195
***
-0.200
***
-0.201
***
(0.0384)
(0.0461)
(0.0458)
0.0155
**
0.0140
0.0139
(0.00567)
(0.00737)
(0.00737)
0.00129
0.00178
0.00179
(0.00113)
(0.00147)
(0.00147)
-0.847
-0.697
-0.697
(0.524)
(0.688)
(0.688)
-0.0203
*
-0.0188
-0.0187
(0.00863)
(0.0112)
(0.0113)
Interaction
term
0.0436
0.0425
0.0423
(0.0402)
(0.0344)
(0.0344)
Covid dummy
-0.758
***
-0.720
***
-0.720
***
(0.117)
(0.148)
(0.148)
Base dummy
1.049
***
0.986
***
0.986
***
(0.105)
(0.133)
(0.133)
_cons
0.0202
-0.00959
-0.00960
(0.0229)
(0.0353)
(0.0349)
N
172
172
172
adj. R
2
0.551
Fixed effect
Yes
Yes
Yes
Standard errors in parentheses
*
p < 0.05,
**
p < 0.01,
***
p < 0.001
VI. Discussion and Conclusion
In this paper, we studied the impact of changes in monetary conditions in the United States on
the performance of the real estate market in Dubai. Since November 1997, the UAE has
adopted a fixed exchange rate policy, where each US dollar is equivalent to 3.67 dirhams, a
policy that simplifies international trade and reduces the risks of investing in international
securities. But on the other hand, one of the shortcomings of the fixed exchange rate policy is
that it includes the automatic follow-up of US monetary policy. Which may require harmony
in economic conditions. This paper is gaining importance in light of the high rates of inflation
around the world after the pandemic that caused disruption in supply chains and the Russian
war and the resort of the US Federal Reserve to aggressively raise interest rates to combat
domestic inflation. It is useful to estimate the impact of monetary tightening in the United
States on the performance of the real estate market in the Gulf region, taking Dubai as a case
study as the previous literature that tackles this topic is quite scarce. There are two main
channels through which the monetary policy in the United States can affect the real estate
market in Dubai: First, the change in the interest rate in the US will trigger a change in the
interest rate in the UAE which in turn affects the interest rates on mortgages increasing the cost
of financing. Secondly, the interest-rate policy is an important determinant of the value of the
US dollar and consequently the dirham relative to other currencies. With the tightening of
monetary policy, the dollar and the dirham gain more strength relative to other currencies,
which increases the relative price of Dubai real estate in currencies such as the euro or the
pound sterling which may weaken the real estate demand.
We collected monthly longitudinal data from the DLD on the volume of monthly
transactions from 2014 to Q1-2022. The data is broken down into two groups, the first group
is the real estate units that were sold directly through the developer to the real estate buyer
without financial intermediation. The second group is the real estate units that were sold
through the mortgage and involve financial intermediation. This classification helps to use the
panel-date analysis controlling for the fixed effect, as mortgage buyers and the type of
mortgage properties might differ from the other group properties. This arrangement has an
additional benefit in identifying the transmission channel through which the changes in the US
interest-rate policy transmit to the real estate sector in Dubai, i.e. the cost of financing channel
or the exchange rate channel.
As expected, our models suggest that there is a strong inverse relationship between the
changes in the interest rate in the US and the demand for properties in Dubai. An increase in
the US interest rate by one percentage point lowers the number of property transactions by
about 17% and vice versa. This finding remains robust across different regression
specifications. As the mortgage interaction term is insignificant, this suggests that the pathway
through which the US interest rate is influencing the real estate market in Dubai is the exchange
rate channel.
This is study is not in a position to evaluate the exchange rate policy. However, the
International Monetary Fund (IMF) suggests that the exchange rate peg is appropriate and
provides a credible policy anchor and stability, and moving away from the peg in the near term
would be destabilizing and have limited benefits for competitiveness (IMF 2022). That being
highlighted, if the economic conditions differ between the two countries, fiscal policy can play
a role in maintaining the competitiveness of the real estate sector. If the US economy is
overheated and inflation is high, while it remained low in the UAE that necessitated the
intervention of the Federal Reserve, fiscal policy can be used in the UAE to neutralize the effect
of contractionary monetary policy. For example, DLD imposes 4% of the total price of a
property as a registration fee upon transfer of ownership of the property. One policy option is
to reduce or waive the registration fees during the time of a strong dollar to mitigate some of
the impacts of the monetary policy tightening. Another policy option to stimulate mortgage
investors during a time of tight monetary conditions is to change the loan-to-value ratio (LTV),
which determines the minimum amount to put in a down payment to get mortgage finance.
The maximum loan amount in the UAE ranges between 75 to 80% of the property value.
The current study is not free of limitations. One limitation is that we do not have data
on the level of properties and their price distribution. Luxury properties might respond
differently to changes in interest rate policy compared to affordable properties. The availability
of individual unit data in the future would facilitate conducting further research to study the
heterogenous response in the real estate market, which will help in designing effective policies.
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