課程二 :不動產經濟 Real Estate Economics

課程重點• 不動產市場上的需求與供給如何決定價格• 不動產需求的估算

– Location quotient

– Basic non-basic method

Real Estate Markets: Understanding Real Estate Supply and Demand

• The interaction of supply and demand sets market prices and rents

• Supply for real estate: • total square feet of all buildings offered for sale or rent give price or

rental rate

• Demand for real estate:• the amount of square feet of space, housing units which buyers wish to

rent or purchase are given rent rate or price

• demand for real estate is derived demand

Short and long run equilibrium adjustments

New demand curve

Old demand curve

Existing supply (fixed)

Quantity of space

Rent/unit

P0

P1

New long run supply curve

P*

Qo Q*

Commercial real estate rental market equilibrium

Pe

Ph

Pl

C D

A B

S = f(P)

Quantity of space (sq.ft.)

Qe

AB = excess demandexcess demand drives up prices

CD = Excess supplyexcess supply causes prices to bebid down

Rent/unit

Pe = equilibrium priceQe = equilibrium quantity

Vacancy rate increases

Vacancy rate decreases

QbQa

D = f(p)

Normal vacancy rate

住宅供給分析民國79年臺灣地區各區域住宅及空屋分佈概況

都會區 住宅總數 空屋數 空屋率北部地區 2,315,127 320,683 13.9

中部地區 1,168,129 149,933 12.8

南部地區 1,442,934 183,275 12.7

東部地區 147,719 20,066 13.5

合計 5,073,909 673,957 13.3

地區 79 87

台灣地區 13.29% 17.24%

台北市 9% 11.72%

台北縣 16.7% 19.68%

台中市 19.69% 21.87%

高雄市 16.18% 21.25%

房價走向綜合分析表

供給面 需求面房價走向 供給量 股價 利率 貨幣供給 銀行存款 中央政府

預算大幅上揚 不變或減少大幅上揚 大幅上揚 大幅上揚 大幅上揚 擴張小幅上揚 不變或減少穩定上揚 較低水準 穩定上揚 穩定上揚 擴張持平 不變 穩定 平均水準 溫和成長 溫和成長 穩定小幅下跌 增加 小幅下跌 中高水準 持平或小

幅成長持平 緊縮

大幅下跌 增加 大幅下跌 高水準 持平或減少

持平或減少

緊縮

What Creates Demand for Real Estate

• Most real estate economists consider employment, not number of people, as the deriving force behind real estate demand --effective demand, not potential demand.

• Employment does the following:

– Create demand for people to work in employers’ firms

– Create the purchasing power to support demand

– Create the need for space to house economic activity

– Create demand for further employment to support businesses

需求推估A Model of Demand for Real Estate

• The following model suggests that there is a linkage between basic employment, general employment , population, and ultimately demand for various types of real estate

• The theory holds that increase in demand for products and services of the basic sector cause the basic industries to employ more people.

• When this happens the non-basic sector must expand to meet the demand of the expanded labor force in the basic industries

• The resulting increases in the number of employed persons, general population, and local purchasing power lead to increase in demand for all types of real estate

BasicEmployment

Total Employment (Basic and Non-Basic)

IndustrialDemand

OfficeDemand

Population/Income

HousingDemand

RetailDemand

A MODEL OF DEMAND FOR REAL ESTATE

Economic Base Analysis• Economic Base: economic activities that enable a city, town,

region to attract income from outside

• Basic employment brings money into a market area as opposed to employment that merely circulates money already in the market -- export oriented industries

• The latter type of employment is called non-basic employment --services and support industries

• Task: Analyze how changes in economic base activities affect future employment, population, future income and thus demand for real estate.

Why Study Basic Industry?• The focus of economic base activities is

employment and income generating activities

• Jobs + People + Income ---> land use or demand for real estate

• Steps:• Predict future employment in basic

• predict future total employment

• Convert population into # of households

• Predict total household income

• Estimate potential demand

Methods of Economic Base Analysis

• Location quotient

• Basic non-basic

• Common characteristics of models• urban economy is open

• stress relationship with outside world

• differ in terms of capturing complex realities of urban areas

Location Quotient• A statistical model used to identify basic industry

• Assumptions of model• No spatial variation in consumption patterns

• No spatial variation in labor productivity

• An industry produces a homogenous good

• LQi = (LEi /LEt)/(NEi /NEt)

• where– LQi = location quotient for industry i

– LEi = local employment for industry i

– LEt = total local employment

– NEi = national employment for industry i

– NEt = total national employment

Location Quotient Analysis• Rules for interpreting LQ

• LQ < 1 ---- non-basic

• LQ = 1 --- non-basic

• LQ > 1 ---- basic or export oriented

• If LQ > 1, determine the number of employees who produce for export as follows

• Xi = (LEi/LEt - NEi/NEt)xLEt

• where Xi = portion of total employment in basic industry i that produces for export

Location Quotient Analysis• Consider the following two industries in a market area, Industry X, Industry Y

Local employment in Industry X = 3,000

Local employment in Industry Y = 5,000

Total employment in market area = 10,000

Total population in market area = 40,000

Total income = $220,000,000

National employment in Industry X = 7,000

National employment in Industry Y = 13,000

Total employment in U.S.. = 100,000

Location Quotient Application• The LQ for Industry X:

LQx = (3,000/10,000)/(7,000/100,000) = 4.28

• LQ for Industry Y:

LQy = (5,000/10,000)/(13,000/100,00) = 3.85

Actual basic employment in Industry X = (.30 -.07)(10,000) =2300

Actual basic employment in Industry Y = (.50 -.13)(10,000) =3700Total actual basic = 2300+3700 = 6000, Total non-basic = 10,000-6000 = 4000

Basic non-basic method• Used to predict the impact of any proposed change in basic emplo

yment on community’s,• total employment

• total income

• total population

• Assumptions of model– Total emp (Et) = Basic emp. (Eb) + Non-basic (Es)

– Change in basic sector leads to change in non-basic

– Ratio of total employment to basic employment can be measured

– This ratio is sufficiently stable to be used in forecasting

Basic Non-basic Multipliers• Employment Multiplier (Ke)

– Ke = Et/Eb

– Alternative method small k = Es/Et thus the employment multiplier is 1/(1-k)

• Population Multiplier (Kp)

– Kp = P/Et , where P = total population

• Income Multiplier– Ky = Y/Et, where Y = total income

Application: Forecasting • Forecasting future total employment

– Employment multiplier Ke = 10,000/6000 = 1.67

– Using alternative method k = 4000/10000 = .4

– multiplier = 1/(1-.4) = 1.67 CHECK

• Basic employment expected to increase by 3000 jobs to E*b = 9000 by year 2000.

• What is the impact on total emp.?

• E*t = (Ke)(E*b) = (1.67)(9000) = 15000

• Similarly we can predict impact on total population and total income by year 2000

Application: Forecasting • Forecasting total Population

– Population Multiplier Kp = 20,000/10000 = 2

– Future population, P* =( Kp)(E*t) = (2)(15000) = 30000

– Increase in population 30000-20,000 = 10000

• Forecasting total income

– Income multiplier Ky =$ 220,000,000/10,000 =$20,000

– Future income Y* = (20,000)(15000) = $300,000,000

– Increase in income = 300,000,000 - 220,000,000 =80,000,000

Demand Analysis• Project Demand for Housing

– Change in population = change in employment/labor force participation rate

– OR, Total pop. = (Kp)(E*t)

– Change in #households = change in pop./household size

– Change in homeowners = (ownership rate )(change in # of households)

– Renters = #of households - change in # of homeowners

• Note: the type of housing desired will depend on income, taste, family size, age, life style preferences, etc

Demand Analysis (continued)

• Estimating demand for office space• Change in office emp. = (% of office workers)(change in total emp.

• Demand for additional office space = (change in office emp.)(office space per employee)

• Demand for retail space: Trade area analysis• Social and economic groupings

• Neighborhood Boundaries

• Travel and shopping patterns

– Total Purchasing power = (# of households)(average household income)

– Total potential sales = (% of income spent on groceries)(total purchas

ing power)

Demand Analysis for Retail• Total Purchasing power (TPP)

– TPP = (# of households)(average household income)

• Total Potential Sales (TPS) – TPS = (% of income spent on groceries)(total purchasing pow

er)

• Total Site Sale (TSS)– TSS = (Site capture rate)(TPS)

• Justifiable Space Demand (JSD)– JSD = (TSS)(Sales per sq.ft.)

Real Estate Market Analysis: An Illustration

• In 1994 Milwaukee county in the state of Wisconsin had total population of 1.5 million. The service sector is important in the economy of the county and employed about 165,466 workers, about 35.2% of the total county workforce of 470,114. Sausage industry employs about 2,924 while finance insurance and real estate employed about 45,470 workers. Over the next six years 1994 - 2000, employment at the county level in the three sectors is expected to increase by 10,693 workers. The average household income for the county is $45,000 and the average household size is 2.5 persons per household. Households typically spend about 8% of their income on retail items and average retail sales per square foot in the county is $300. Employment in service sector in the state of Wisconsin is 536,208 and total state employment is 1,948,856. At the state level the sausage industry employs about 6,448 workers and finance insurance and real estate employs 129,017 workers.

What are the questions to be answered?

• Given the information in the case we wish to analyze how the predicted increase of 10,693 workers from 1994 to 2000 in the service, sausage and finance, etc industries, affect (1) future total employment, (2) future population and (3) future income. Ultimately, we also want to know how these changes affect demand for grocery space.

Location Quotient Analysis• Location quotient analysis

Service sector:

LQv = 165,466/470,114)/(536,208/1,948856) = 1.279

• Sausage industry:

LQs = (2,924/470,114)/(6,448/1,948,856) = 1.880

• Finance, insurance and real estate:

LQf = (45,470/470,114)/(129,017/1,948,856) = 1.461

• Note: all three industries are basic.

How many workers actually produce for export

• Actual basic employment in each industry

• Service:

Xv = (.35197 - .27514)(470,114) = 36,118

• Sausage:

Xs = (.00622 - .00331)(470,114) = 1,368

• Finance, insurance, real estate

Xf = (.09672 - .06620)(470,114) = 14,348

• Actual basic (all 3 sectors) = 51,834 (Eb)

Non-basic = 418,280 (Es)

Total employment = 470,114 (Et)

Basic Non-basic Multipliers

• Employment Multiplier( Ke )

= 470,114/51,834 = 9.0696

• Population Multiplier (Kp)

= 1,500,000/470,114 = 3.1907

• Income multiplier (Ky)

= $27,000,000,000/470,114 = 57,432

What is the impact of change in basic employment of 10,693

• Future total employment (E*t) , in year 2000

E*t = (9.0696)(62,527) = 567,095

Total increase = 567,095 - 470,114 = 96,981• Future total population (P*)

P* = (3.1907)(567,095) = 1,809,430, by year 2000

Change in pop. 1,809,430 - 1,500,000 = 309,430• Future total income (Y*) , by year 2000

Y* = (57,432)(567,095) = $32,545,800,000

Change in income = $5,545,800,000

Number of households and average household income

• Number of households = 1,809,430/2.5 = 723,772 , by 1999

• Average household income = $32,545,800,000/723,772 = $44,966, by yr. 2000

• Note: Despite the increase in total employment average household income is predicted to be about the same as the 1994 level.

Estimating potential demand for retail space • Total Purchasing Power(TPP) = number of Households x

average household income = $32,545,000,000

• Total Potential Sales (TPS)= percent spent on groceries x TPP = (.08)(32,545,000,000 = $2,603,600,000

• Potential Sales at Site (PSS) = site capture rate x TPS =(.5)(2,603,600,000 = $1,301,800,000

What is the net increase in space over the 6-year period?

• Change in income or purchasing power = $32,545,000,000 (1999) - 27,000,000,000 (1994) = $5,545,800,000

• Change in total potential sales (CTPS) = (.08)(5,545,800,000) = $443,664,000

• Change in Potential Sales at Site (CPSS) = Site capture rate x CTPS = (.5)(443,664,000) = $221,832,000

• This 50% capture rate assumes that neighboring counties have equal chance of capturing the increase in retail demand as Milwaukee county.

Estimating Potential Demand for retail space

• Remember sales per square foot = $300• Justifiable additional demand for space = 221,832,000/30

0 = 739,440 sq.ft. new space• Thus over the six year period Milwaukee county will need

an additional 739,440 sq.ft.

• Existing space = 4,339,333 - 739,440 = 3,599,893 sq.ft.• New space = 739,440 sq.ft.• Total sq.ft. = 4,339,333 sq.ft.

Demand for Housing• Increase in population= 309,430• Increase in households = 123,772• New homeowners = homeownership rate)(change in

households) = (.647)(123,772) = 80,080

• New renters = 123, 772 - 80,080 = 43,692

• Note: The homeownership rate of 64.7% is a national average.

Criticisms of the Models

• Location Quotient• Uniform consumption pattern is not strictly valid;

• Uniform productivity is not valid either; and

• Interpretation of LQ in some cases does not make sense.

• Basic non-basic• Excessive aggregation

• Multiplier is an average response

• Instability of multiplier in short run (lag effect), in long run (changes in pop., tech., taste)

住宅需求分析• 住宅需求 =f(P,E,C,F,D1,D2,D3)

P= 人口成長E= 家庭收入C= 住宅價格F= 戶量變動D1= 逾齡住宅拆簽數D2= 舊有違建拆除戶D3= 公共工程拆除戶

Source: 張偉斌 ,“ 住宅需要與投資之探討” , 臺灣土地金融月刊 ,84 年 12 月

年 總人口(千人)

就業人口數(千人)

每人月平均薪資(元)

1997 21683 9566 388181996 21471 9428 365901995 21304 9272 352681994 21126 9207 351471993 20944 8891 321261992 20752 8874 304341991 20557 8670 278921990 20353 8500 25120

臺灣地區人口統計變數

Year Average annual disposable incomePer household(NT$)Housing price(per PIN)1987 366,487 100% 12.11 100%1988 410,483 112% 26.98 223%1989 464,994 127% 32.57 269%1990 520,147 142% 32.64 270%1991 587,242 160% 30.97 256%1992 639,696 175% 36.81 304%1993 727,879 199% 35.2 291%1994 769,755 210% 32.57 269%1995 811,338 221% 38.58 319%1996 826,378 225% 34.57 285%

0%50%

100%150%200%250%300%350%

1985 1990 1995 2000

家戶支出

房價