MPM Capstone Proposal
What are the appropriate rental rates for apartment
housing in Beijing?
Shang Lin
Master of Project Management Program
Department of Civil and Environmental Engineering
McCormick School of Engineering and Applied Science
Northwestern University
Evanston, Illinois
Oct 15, 2015
Title: What are the appropriate rental rates for apartment housing in Beijing?
Abstract: Beijing residential housing market shows an imbalance between demand
and supply. While some people cannot find a place to live, other people have empty
houses. A good way to solve this problem is to encourage landlords rent their vacant
houses to those in need.
demand and supply side.
An appropriate rental rate will further motivate both
This article will establish a regression model based on data
from last ten years to solve the problem.
Key Words: rental rates, residential, Beijing
Contents
1 Context and literature review ................................................................................................. 4
1.1 Context and Purpose .......................................................................................................... 4
1.2 Literature Review ............................................................................................................. 10
2. Model Introduction ................................................................................................................. 13
2.1 Linear regression .............................................................................................................. 13
2.2 Least Squares ..................................................................................................................... 14
3. Regression Analysis ................................................................................................................. 15
3.1 Data ........................................................................................................................................ 15
3.1.1 Rental rate ..................................................................................................................... 15
3.1.2 Variables of real estate market ............................................................................... 15
3.1.3 Variables of economy ................................................................................................ 17
3.1.4 Variables of population ............................................................................................. 17
3.2 Regression Analysis.......................................................................................................... 18
4. Conclusion .................................................................................................................................. 20
4.1 Important factors.............................................................................................................. 20
4.2 Model Flaws ........................................................................................................................ 22
5 Reference list ............................................................................................................................... 22
1 Context and literature review
1.1 Context and Purpose
Beijing’s rental apartments market is experiencing great imbalance. On the one
hand, new generation of immigrants and young couples are struggling to find place to
live. On the other hand, many wealthy people complain that they have empty houses
with on one rent.
In order to solve this problem, I suggest look into the rental rate.
An appropriate rental rate might be a good way to adjust the market.
Before setting
up a new rental rate, we need to figure out what are the major factors affect rental rate.
This article is going to run a factor model based on data of residential market from
last ten years to find out.
The imbalance started from China’s urbanization.
Similar to other big cities in
China, Beijing is influenced by the acceleration of China's urbanization.
The urban
population is growing, urban land is scarce, and the city population density is
increasing.
Urban housing, one of the biggest problems come after urbanization,
attracts increasing concern from the public, due to its low elasticity and its shortage.
Beijing, where the ultra-high housing prices are already beyond quite a few people’s
affordable range, leaves renting as the only remaining option.
more problematic.
However, renting is
Both the demand and the supply of rental apartments in Beijing
are enormous, but the vacancy rate is still high.
In order to coordinate demand and
supply, an appropriate rental rate is necessary.
To begin with, Beijing, one of the most developed cities in China, is attracting
thousands of immigrants each year.
According to the sixth population census
（“sixth population census”, 2011）: the city's resident population is 1961.2 million,
an increase of 604.3 million compared with the fifth national census in 2000, an
increase of 44.5%.
The average annual increase is 60.4 million people, and the
annual average growth rate is 3.8%.
Showed in Figure 1-1, Beijing is experiencing
sharp population growth since 2005, with the average growth rate of 13%.
Figure 1-1.
Beijing Population Growth Chart.
(“Beijing population boost”,
2015)
Among the city's whole residential population, 704.5 million are immigrants,
accounting for 35.9% of the total.
And according to Figure 1-2, most immigrants
settled down in central Beijing, which is only 4% of total Beijing area.
With so
many new people moving into such a small area, residential housing became
problematic. As traditional Chinese culture encourages people to buy their own
house, the housing price of Beijing has risen to an unaffordable level due to the high
demand.
Figure 1-2 Beijing Population Distribution Chart.
(“Beijing population
distribution”, 2015)
As we can see from figure 1-3, the housing prices increased to $9,000 per square
meter. And the center of Beijing, shown in figure 1-4, has the highest housing price
of more than $10,000 per square meter.
The average wage level in Beijing,
nonetheless, is only $10,862 per month according to People’s daily (Feng & Lu,
2014). So it will take people 50 years to earn enough money to buy a two-bedroom
apartment without any consumption.
Due to the unaffordability of buying houses,
new immigrants have to rent apartments, increasing the demand for rental housing.
RMB/ square meters
Beijing Housing Price
Figure 1-3.
60000
50000
40000
30000
20000
10000
0
The trend of Beijing Housing Price.
(“The trend of Beijing
Housing Price”, 2014)
Figure 1-4.
Beijing house price map.
(“Beijing Housing Map”, 2014)
Apart from that, the rising price of Beijing’s residential housing became the force
to increase rental apartment’s supply.
on its way.
Firstly, a large amount of new construction is
In 2013, residential land use growth is 135,732,910 square feet (Liu &
Qiu, 2014), 42% of year before.
And Beijing Municipal Bureau of Land and
Resources stated that they plan to keep such a growth rate in order to meet the
housing needs. As showed in figure 1-5, the darkest area, which stands for
completion construction, is rising steady each year. However, the top grey area,
which stands for the housing needs, is still much larger than the supply. Thus we can
predict that the supply will keep growing in the following years to fix the gap between
demands.
Figure 1-5 Beijing Housing Supply.
(“Beijing residential policy research”,
2014)
Though most of new built houses are sold, some go to the rental market
eventually. As the house price goes up, wealthy people showed their thirsty toward
real estate.
More and more people consider fixed assets an extremely profitable
investment, starting to buy as many houses as they can.
of their houses since they can only live in one.
portion of rental housing.
Then they have to rent some
Figure 1-6 below illustrates the
As we can see, the rental housing is about 18% of total
housing since 2007. Households live in rental apartments are around 900,000.
Figure 1-6 Beijing Rental Housing Portion Chart.
(“Beijing residential policy
research”, 2014)
Those who do not have their available house rented, leave them vacant.
Therefore, the vacancy rate in Beijing is estimated at a high-level about 30%, much
higher than 5%(Gstach, 2007), which is usually considered as a healthy level. Figure
1-7 showed the vacancy rate. The first chart is the vacancy rate by house type.
Normal housing has the highest vacancy rate of 26.3%. Affordable housing ranks
the second with a rate of 23.3%. Most other special housing also have high vacancy
rate of over 10%. The second chart is the vacancy rate by household’s income.
Generally speaking, higher income level families have more houses, resulted in higher
vacancy rate.
Figure 1-7 Beijing Housing Vacancy Rate.
(Liu, 2014)
All in all, Beijing residential housing market shows an imbalance between
demand and supply. While some people cannot find a place to live, other people
have empty houses.
A good way to solve this problem is to encourage landlords rent
their vacant houses to those in need.
An appropriate rental rate will further motivate
both demand and supply side.
1.2 Literature Review
Plenty of scholars have conducted thorough research about reasonable rental rate
levels.
However, the results lead to different preference.
At same time, some
scholars also argue that finding out factors that affect landlords and tenants’
preference is more important.
A summary of literature review is listed below.
To begin with, some scholars believe the problem is that the rental rates of most
apartments are too high.
In 2010, rental rate increase nearly 20%, much faster than
the increase rate of wage (Song, 2013).
harmful.
The consequence of this high price is
On the one hand, a large number of immigrants are looking for rental
housing desperately. On the other hand, landlords are losing money due to the high
vacancy rate.
Ambrose (Ambrose, 2002) affirmed in his study that a lease with
maximum value has lower vacancy rate. Even an upward priced lease cannot
compensate landlord’s loss at beginning.
Research conducted by Allen (Allen,
Rutherford, & Thomson, 2009) found that setting an asking price too high may impair
leasers by increasing the time on market of their apartments and usually ending up
with a lower contract rent.
There is also a study (Einiö, Kaustia, & Puttonen, 2008)
showed that lower housing prices are more common and beneficial.
Besides, an
article (Vakili-Zad & Hoekstra, 2011) pointed out that in some immature market high
rental rate would not self-adjust according to the vacancy rate.
condition can last long.
Thus the unfavorable
Later, some Chinese scholars found comparable results in
their researches. A study (Ju, Yu, & Zhou, 2013) looked into the relationship
between house price and vacancy rate and conclude that high price and high vacancy
rate resulted in a vicious circle of the residential market.
Zhang (Zhang, 2011)
believed that high vacancy rate is a warning signal for real estate bubble.
indicated that speculated investment is the major cause.
He also
Another article (Xu, 2013)
advocated that government step into the market to impose vacancy tax to ease the
harmful situation.
However, there are some scholars believe in high vacancy rate as they listed
several beneficial situations.
Lind (Lind, 2008) mentioned in his article that though
usually resulted in higher vacancy rate, higher rental rates are major source of energy
system investment.
With higher rent, landlords have the motivation to adopt
eco-friendly heating system in their apartments, which will eventually improve the
environment.
There is another research (Bouzarovski, Salukvadze, & Gentile, 2011)
argued that high vacancy rates encourage landlords to improve living condition, such
as redecorating balconies.
Miceli and Sirmans (Miceli & Sirmans, 2013) believed
that high vacancy rate is favorable. They stated that a positive vacancy rate actually
encourages landlords to invest in the maintenance of their apartments, which will
generate higher rents in the future.
Pivo (Pivo, 2014) agreed with Lind in his
research with the example of the energy system of US multifamily rental housing.
And there are some Chinese scholars (Wang & Yu, 2013) arguing that the rental rates
are low, compared with the price of purchasing houses.
They cite the fact that the
annual rental income for most leasers is even lower than deposit interest.
Also,
Wang(Wang, Hong, & Long, 2008) added that the vacancy rate is mainly decided by
the demand and supply of the market.
Therefore no regulation is required.
Due to these disagreements, it is reasonable to come up with a model in order to
figure out appropriate rental rates.
According to the research done by several
Chinese scholars (Chen, Wang, & Bell, 2014), apartments can set appropriate rent
annually in order to pursue a certain level of revenues.
A study from Duan (Duan,
2014) added that appropriate rental rate can also be measured by discounting house
price. Other scholars contribute to find out factors that may influence rental rate.
First study came from Cheung (Cheung, 1995).
He found out the positive
relationship between property transaction price and rental rate.
It seems that higher
property price squeeze people into rental market and push rentals up as a result.
Later, a study from Xu (Xu, 2010) established a model to measure the relationship
between house price and rental rate. Xu treated house price as the cost of landlords
and rentals the return.
years.
cost.
An appropriate rental rate should cover the cost in one to two
Liu (Liu, 2011) continued Xu’s study by separated land cost from the total
He explained that land price is a different but important component.
Chen
(Chen, 2011) further confirmed their study by redoing Xu’s model but combining
Liu’s idea. Besides, he take income rate into account as well.
However, Wang
(Wang, 2012) disagreed by asserting that house price should not be regarded as an
important factor as it only affect rental rate indirectly.
Belsky (Belsky, 2010) set
house type and location two major factors to be considered when pricing apartments.
Yan and his team (Yan, Feng, & Bao, 2010) found from research that land supply and
financial regimes are determining factors for long-term equilibrium.
mentioned mortgage rate as another essential feature.
They also
Tang and Li (Tang & Li, 2013)
indicated that acceptable rental is highly influenced by household’s consumption,
which can be found in CPI. The higher portion spend on living goods, the lower can
be spend on rents.
At same year, there is another study (Xing, 2013) pointed out that
income of landlords should also be considered.
As their decision of whether invest
into real estate may highly influence the supply side.
Yang (Yang, 2014) stated that,
from tenants’ side, government subsidy can be used to lower rental rate to acceptable
level.
Wang (Wang, 2015) claimed that subtle factors should not be neglected as
they can make big difference. For example, a bus stop near the apartment can rise
the rental a lot higher. And land value tax should be considered as well.
In order to set up an appropriate rental rate, this research will look deeply into the
influence of rental rate to vacancy rate as well as other factors that should be
considered when setting rental price.
In regards to the landlords, factors will include
interest rate and expectations of housing prices.
As for tenants, factors will include
income, consumption rate, CPI etc.
2. Model Introduction
2.1 Linear regression
Linear regression is a model used to describe the linear relationship between
dependent variable and explanatory variables. A linear model will be used in this
article to explain the relationship between rental rates and relevant factors.
A linear model assumes that the relationship between independent variables
and dependent variables is linear.
The form of regression model is like
In this article, yi
represents the rental rates while xi are the influential factors.
unpredictable part of rental rates.
i
εi is the
is the coefficient, explaining the rental’s change
rate based on each independent variables.
Liner model can be used in this article
since all the independent variables affect rental rate directly. Therefore, the
relationship is linear.
Linear regression models are often fitted using the least squares approach, which
will be explained further in detail in 2.2.
2.2 Least Squares
Least squares is a method used in this article to find coefficients that fit the
model best.
The theory behind this method is that the best model is the line
which has the minimum sum distance to each dot.
Thus, the least squares calculate the sum distance
and finds the minimum one.
from each dots
A residual is defined as the difference between the
actual value of the dependent variable and the value predicted by the model.
Least squares is a widely used method in analyzing linear model since it is
accurate and efficient.
We will adopt it in our regression analysis.
3. Regression Analysis
3.1 Data
All the data is retrieved from Beijing Real Estate Yearbook. Considered the fact
that Beijing’s urbanization starts in 2000, data after year 2000 is more typical and
reliable. Thus, we use 2003-2013, a decade as our study
3.1.1 Rental rate
rental
rate($/sf)
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
44
46
49
50
91
93
82
98
106
114
123
Chart 3-1 Rental rates (Data source: Beijing Real Estate Yearbook)
Rental rate, the dependent variable in our analysis, is listed above.
As we can see, the
rental rate kept rising in the past decade from $44/sf to $123/sf, with an increase rate of
10.78%.
The most rapid increase happened in 2007, with an increase rate of 82%.
(Increase rate calculated as (yeart/yeart-1 -1)*100%)
3.1.2 Variables of real estate market
year
2003
2004
2005
2006
house
price($/sf)
8834
10660
17466
17951
Land
price($/sf)
7574
7828
8123
8788
Annual
construction
area for
residential(10000 vacancy
sf)
rate(residential)
97056.49
9.89%
106264.91
7.29%
115008.95
7.44%
112173.45
4.71%
2007
2008
2009
2010
2011
2012
2013
18436
20649
22920
24983
25233
25737
29855
10855
9668
9898
13908
15094
15030
15331
111693.02
107153.01
103994.37
110219.63
129099.78
140410.75
148589.83
3.94%
5.22%
4.39%
4.97%
5.80%
6.02%
5.97%
Chart 3-2 Variables of real estate market (Data source: Beijing Real Estate Yearbook)
The house price, land price, annual construction area and vacancy rate from 2003 to
2013 are considered as independent variables that influence rental rates.
House price is a relevant factor since the driving demands of rental market and condo
market are highly overlapped and can easily switch to each other.
As we can see from the
chart 3-2, the house price rise in last decade with 2004 and 2005 had the most rapid
increase rate of over 20%.
In 2006, 2007, 2011 and 2012 the house price remain steady
with increase rate lower than 3%.
In regard of land price, which usually takes one third of development cost, experienced
growth as well.
of 2009.
Though the average growing rate is 8.09%, 2010 land price raised 40.52%
The reason of this sharp increase is political since income from selling land is used
to fix government deficit.
Construction area stands for the supply of housing market.
As discussed in previous
paragraph, even most new construction houses are sold, those bought by speculators finally
go to rental market.
Therefore, we can see an indirect relationship between new
construction area and rental rates.
The chart shows that new construction area has
increased steady with average increase rate at about 5%.
Vacancy rate fluctuated in last decade from 3.94% to 9.89%.
5.97%, a little bit higher than normal level.
The average vacancy rate is
3.1.3 Variables of economy
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
annual
income($/person)
2337.39
2674.45
3052.08
3502.66
3840.00
4324.69
4792.81
5212.50
5800.63
6422.34
7074.06
average
living
area(per
GDP(based
person/sf) on 1978)
CPI
210.897
1125.8
631.9
229.943
1284.5
638.2
235.721
1440.3
647.8
253.055
1627.5
653.6
265.039
1863.3
669.3
287.83
2033.0
703.4
296.283
2240.4
692.8
309.658
2471.2
709.4
314.366
2671.4
749.1
313.082
2877.1
773.8
335.017
3098.6
799.3
interest
rate(one
year
deposit)
1.98%
2.12%
2.25%
2.39%
3.47%
3.38%
2.25%
2.63%
3.25%
3.13%
3.25%
Chart 3-3 Variables of economy (Data source: Beijing Real Estate Yearbook)
Other factors that influence macro economy can affect rental rates as well.
Chart 3-3 Considered annual income, average living area, GDP, CPI, interest rate.
Annual average income grew at 12%, faster than rental rates, indicating that people
can bear higher expenditure on housing.
Average living area has generally increased
from 2003 to 2013, but slowed down its rising speed after 2008.
Beijing expanded
construction area for Olympic Games in 2008, which explained the difference in
increase rate.
GDP is another factor on behalf of macro economy.
developed fast in last decade.
Beijing
CPI, standing for the price level, increased 3%.
In
order to figure out the real increase rate of other factors, we need to take inflation into
account.
3.1.4 Variables of population
permanent population(10000 persons)
2003 1456.4
2004 1492.7
2005 1538.0
permanent migrant(10000 persons)
307.6
329.8
357.3
2006
2007
2008
2009
2010
2011
2012
2013
1601.0
1676.0
1771.0
1860.0
1961.9
2018.6
2069.3
2114.8
403.4
462.7
541.1
614.2
704.7
742.2
773.8
802.7
Chart 3-4 Variables of population (Data source: Beijing Real Estate Yearbook)
As we can see from the chart 3-4 above, both permanent population and permanent
migrant have increased in last ten years. Permanent population has increased 3.81%
while the average increase rate of permanent migrant is 10.17%. As permanent
migrant represents urbanization. The fastest urbanization happened in 2006 to 2010.
3.2 Regression Analysis
Input all the data into SPSS. Set rental rates as dependent variables, other 11
factors as independent variables.
However, the first regression result is not satisfied.
The reason behind this is that there are too many factors, each explain a small portion
of dependent variables.
We need to re analyze to figure out the most important
factors instead of use them all.
Re-analyze with less independent variables.
Several tests show that CPI,
vacancy rate and land price explain the rentals the most.
Figure 3-2-1 Variables Entered
Model Summaryb
Adjusted
R Std. Error of the
Model
R
R Square
Square
Estimate
1
.965a
.930
.901
9.249
a. Predictors: (Constant), CPI, vacancyrate, landprice
b. Dependent Variable: rental
ANOVAb
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
8001.982
3
2667.327
31.182
.000a
Residual
598.779
7
85.540
Total
8600.761
10
a. Predictors: (Constant), CPI, vacancyrate, landprice
b. Dependent Variable: rental
Figure 3-2-2 New Model Summary
The new regression analysis has R square 0.93 and adjusted R square 0.901.
Also the sig<0.1.
We can say that the new model is significant.
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
-141.492
73.949
landprice
.003
.003
vacancyrate
-346.372
CPI
.302
Model
1
t
Sig.
-1.913
.097
.316
1.158
.285
180.952
-.201
-1.914
.097
.140
.583
2.150
.069
a. Dependent Variable: rental
Figure 3-2-3 Coefficients
Residuals Statisticsa
Minimum
Maximum
Mean
Std. Deviation
N
Predicted Value
37.11
124.48
81.42
28.288
11
Residual
-14.586
12.118
.000
7.738
11
Std. Predicted Value
-1.566
1.522
.000
1.000
11
Std. Residual
-1.577
1.310
.000
.837
11
a. Dependent Variable: rental
Figure 3-2-4 New Residuals Statistics
From the Coefficient chart, absolute t>1, all factors are significant in the model.
Rentals=0.003*landprice-346*vacancy rate+0.302*CPI
Figure 3-2-5 Plot of regression standardized residual
The figure 3-2-5 uses plots to show the value of each year and how does the
model fit the data.
4. Conclusion
4.1 Important factors
The regression analysis above shows that three major features that influence
rental rates are land price, vacancy rate and CPI.
An appropriate model to set rentals
is Rentals=0.003*landprice-346*vacancy rate+0.302*CPI
Land price affect rental market in an indirect way.
Land price usually makes up
one third of construction cost of residential building. Therefore, we can use land
price to calculate housing price.
Condo housing, which is a substitute of rental
housing, is considered as one of the most relevant variable.
However, government
put a ceiling on house price to make it affordable for most families while sometime
enact policy to stimulate housing market in order to pursue higher GDP.
housing price became less predictable as a result.
Condo
For that reason, we use land price
as an indirect indicator of housing price to explain fluctuation of rental rates.
The
coefficient of land price is 0.003, reflecting a positive relationship between land price
and rental housing.
If land price increases, condo housing is likely to become more
expensive.
People cannot afford buying houses immediately keep renting
apartments.
With higher demand, rental rates go up.
Vacancy rate displays negative relationship with rental rates.
related to the bargain power of landlords and tenants.
is buyer’s market.
Vacancy rate is
When vacancy rate is high, it
Tenants can negotiate with landlords to lower rentals.
vacancy rate is high, however, it changes to seller’s market.
When
Landlords can set
higher asking price while tenants have to take it since available houses are scarce.
Hence, the coefficient of vacancy rate is negative, -346.
Rental rates are also related to CPI, which stands for price level or inflation.
CPI is calculated as price level of a basket of goods in current year/price level of the
same basket of goods in base year.
As for CPI in China, house is not included in the
basket of goods, thus no duplicate calculation.
In regards to the relationship between
rentals and CPI, as general price level increased, landlords want more rental income
to compensate their expenditure.
Rentals are pushed up consequence.
The
coefficient of CPI is positive, 0.302.
In sum, to set an appropriate rental rate, landlords should look into land price,
vacancy rate and CPI.
Landlords have more bargain power in rental market overall.
For tenants struggling to find nice and cheap apartments, the hope lies in that new
construction increases faster than immigrant does.
4.2 Model Flaws
The model has several flaws that could lead to deviation of the conclusion.
of all, the model only analyze data from past ten years.
First
The lack of data may add
weight to extreme situations, resulting in estimate value larger or smaller than usual.
Secondly, we use average rental rates as dependent variable.
Nevertheless, each
apartment has different maintenance, decoration and location. These factors are not
reflect in the model due to the absence of such information.
To improve the model, we can use the quarterly data instead of yearly and
conclude more variables into the regression function.
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