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Overhead Set#2: VALUATION APPROACHES

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- 3 Approaches to Valuation:
- Cost Approach
- Income Approach
- Sales Comparison or Market Approach

a. cost of duplicating property minus depreciation

b. most fruitful in engineering or cost-to-cure cases

c. potential problems in ranking projects

1. new vs. established structures--different risks from being leased up

2. non-viable structures: e.g., the World Trade Center in Kansas City

a. capitalizes cash flow as perpetuity via a cap rate

1. V=I/r

where V=property value, I=stabilized income flow, r=cap rate

b. cap rates vs. discount rates

1. cap rate reflects purely real estate/property factors

a. e.g., site-specific factors, property-type issues, property-specific factors

2. discount rates reflect opportunity cost of capital as learned in introductory finance

1. Direct Capitalization

V = NOI / R

2. Present Value Method

V = NOI / (r-g)

r = “competitive” discount rate

g = growth rate

R = r-g

3. Mortgage/Equity Method

V = D + E

D = mortgage debt

E = equity

Building Cash Flow = $100,000/yr for 25 yrs.

Loan Amount = $700,000 at 10% for 25 yrs.

Pmt = $77,177.65

Required Rate of Return = 15%

Investor’s Cash Flow = $100,000 - $77,177 = $22,882.35

Present Value = $147,915

Value = $700,000 + $147,915 = $847,915

IRR = 10.91%

Cap Rate = NOI/V = $100,000/$847,915 = 11.79%

Assumption: NOI remains level

- Assume that AA noncallable corporate bonds with ten years to maturity currently are yielding 10%. Further assume that the real estate cap rate is 7% for an entirely owner-occupied office building with a standard lease whose occupant also has a AA credit rating. [If the building owner were to come to you and propose a sale/leaseback with a ten year term, you would consider the AA corporate bond rate as more appropriate than the real estate cap rate for determining the present value of the cash flows on the sale/leaseback.]

c. potential problems if cash flows are not stable--

large differences in PVs of equal dollar flows if one is cyclical and the other is not

d. potential problems from no attempt to differentiate among the components of cash flow by risk level

e. in practice, often very ad hoc assumptions made in determining I

a. identify comparable properties and value accordingly

b. how should this be implemented empirically?

e.g., how to value a warehouse in terms of its value for conversion to condominiums?

(means implicit in the Greek)

a. used first by consultants to the auto industry

b. simple regression analysis

1. HPi = a + bjXij+ ei ,

where HP is the price of the ith home,

X is the vector of house traits

a is the regression intercept term

b is the coefficient vector of trait prices

e is the error term

2. b = HP/X

the marginal effect of a small change in trait j on home price HP

3. Potential problems with the statistical approach

a. specification error--never know all the relevant traits

b. some traits very hard to quantify--e.g., style features

c. changes in trends in neighborhood from which sample of comparables is drawn

in absence of transactions-based prices, any approach gives you at best an educated guess about asset value; market approach is the best from a conceptual perspective (simple market approach valuation now required in house appraisals by secondary market agencies)

- Simple Real Estate Example:
- We wish to explain/predict the price of single family homes (find their value).
- Dependent Variable (Y) = Sales Price

- Independent Variable (X):
- What causes sales prices to vary?
- Size of property?
- Test to see if sales prices change as property size changes.
- X = size in square feet.

- SCATTER PLOT:
- diagram that plots the relationship between two variables
- In our case, we wish to plot the relationship between sales price and property size.

- Linear model is estimated by least squares
- Ordinary Least Squares (OLS)

- Add independent variables to model to explain more of the price.
- # of bedrooms
- # of bath rooms
- year built
- location
- amenities
- fireplace, garage, deck, etc..

- Problem: Collinearity
- As you add variables, some of the independent variables may be collinear with each other.
- Example: As the number of bed rooms increases, you expect the number of bathrooms to increase as well.
- This will bias your regression equation. (Makes it difficult to support the results in court.)

- As you add variables, some of the independent variables may be collinear with each other.

- Factors that influence warehouse valuation.
- building size
- office space
- ceiling height
- doors (dock and drive-in)
- rail service
- sprinklers
- age