Notes Real Estate Inflation

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Notes Real Estate Inflation Powered By Docstoc
					   Investing in single family

Kevin C.H. Chiang

   The determinants of home price
   No-arbitrage between owning and

   There will be an assignment at the end
    of the topic.
   Please download article #2 from the
    course website. Be prepare to discuss
    the paper when we meet next time.
Popular investments

   Investing in single family housing is
   In U.S., close to 70% of households
    invest in single family housing; about
    30% of households rent a house or
   Benefits of owning a house: financial
    and emotional.
Is investing in a house a good

   Financially speaking, yes and no.
   On average, the appreciation rate based on
    purchase price is close to than that of T-
   But the built-in high leverage via mortgage
    can make the return on equity substantial.
   If one uses the same leverage on other
    investments, houses suck (unconditionally).
   How about conditionally?
   Emotionally speaking, houses rock.
Location, location, location

   The rate of price appreciation is
   During the 2004-2007 period, the
    median sales price of existing homes
    in Riverside, CA went up about 30%.
   During the same period, the median
    sales price in Pittsburgh went down
    about 3%.
The determinants of appreciation
   Population growth (+).
       Immigration accounts for about 1/3 of U.S. population
       Immigrants tend to live in sunbelt cities. Sunbelt cities
        have been enjoyed the greatest home price appreciation.
   Employment (+).
   Household income (+).
   Interest rate (-).
       Higher interest rate = higher cost of owning a house =
        lower house price.
   Cost of renting housing (+).
       But this causality runs both way.
Economic base
   Regional population, employment, and
    income is a function of the regional
   Riverside has a strong economy. This leads
    to higher population, employment, income,
    and home price.
   Pittsburgh runs the other way.
   Thus, it is important to identify and evaluate
    regional economic drivers (economic base)
    when investing in a home.
The supply side

   The previous discussions focus on the
    demand side of housing.
   The supply side is also important:
       Cost of land
           Land-locked: Flagstaff.
           Sea-locked: Honolulu.
       Cost of labor
       Cost of materials
       Development restrictions
Submarket factors

   Appreciate when net benefits are
    created: services received have a
    value greater than the taxes and fees
    paid for them.
       A new, nice public school just built in your
   Rezoning.
   Etc.
Home price too high?

   We do not have a good equilibrium asset
    pricing model for pricing a house.
   The previous demand-supply discussions
    are quite general; we do not have a formula.
   One way to have a formula is to use a no-
    arbitrage relationship: the cost of using
    (owning) a home = the cost of renting a
Cost of ownership, I

   The (annual) cost of owning a house has 6
   1. Opportunity cost: the cost of foregone
    return that the homeowner could have
    earned by investing in something else.
       A conservative measure: the price of housing
        times the risk-free rate = p × rf.
   2. Cost of property taxes: p × w, where w is
    the property tax rate.
Cost of ownership, II
   3. The tax deductibility of mortgage interest
    and property taxes (-): p ×  × (rm + w),
    where  is the (marginal) effective income
    tax rate, and rm is mortgage interest rate.
   4. Maintenance (depreciation) costs as a
    fraction  of home value: p × .
   5. Expected capital gain/appreciation (or
    loss) (-): p × g, where g is the appreciation
   6. An additional risk premium to compensate
    homeowners for the higher risk of owning
    versus renting: p × .
Cost of ownership, III

   Annual $ cost of ownership = p × rf + p × w
    – p ×  × (rm + w) + p ×  – p × g + p × .
   Because every term is a function of p, we
    can write the cost as a percentage of p (we
    call it the user cost of housing):
   User cost = rf + w –  × (rm + w) +  – g + .
Cost of renting

   Annual $ renting costs: R = p × r. Let
    us call r the rent rate, i.e., the ratio of
    the rent to the house price.
   Annual $ renting cost = annual $ cost of
   R (= p × r ) = p × rf + p × w – p ×  × (rm +
    w) + p ×  – p × g + p × .
   That is, the rent rate (the inverse of the
    price-to-rent ratio) must equal the user cost.
   (R / p =) r = rf + w –  × (rm + w) +  – g + .
   The lower the user cost, the higher the
    price-to-rent ratio.
An example
   r = rf + w –  × (rm + w) +  – g + .
   Suppose rf = 4.5%; w = 1.63% (VT);  = 25%; rm =
    5.5%;  = 2.5%; g = 3.5%;  = 2%.
   The user cost = 4.5% + 1.63% – 0.25 × (5.5% +
    1.63%) + 2.5% – 3.5% + 2% = 5.3475%.
   For every dollar of house price, the owner pay
    5.3475 cents per year in cost.
   An investor will be willing to pay up to 18.7 times (1
    / 0.053475) the market rent to purchase a house.
   If the market rent is 4% and our inputs are correct,
    do houses look expensive? What if 6%?
Some analyses, I
   r = rf + w –  × (rm + w) +  – g + .
   Now, let us hold all else equal and look at
    one variable at a time.
   If interest rates drop, what would happen to
    house prices?
   If income tax rate is raised, what would
    happen to the user cost?
   If investors anticipate high price
    appreciation, what happen to the user cost?
Some analyses, II
   r = rf + w –  × (rm + w) +  – g + .
   Suppose you buy houses and rent them out.
    If you expect a high price appreciation,
    would you accept a lower rent?
   Some cities, e.g., SF, Boston, NYC, LA,
    have been characterized by a consistent,
    high price-to-rent ratio for the past several
    decades. Why?
   This makes price-to-rent (or price-to-
    income) a poor measure for judging whether
    house prices are too high.
Appreciation rate, g
   In U.S., the nominal appreciation rate is
    about 3.5% (this varies a lot across cities
    and over time), which is slightly above
    inflation rate.
   Construction costs grow less than inflation
   Thus, land is appreciating faster than the
    structure (building).
   In other words, if you would like to bet on
    single family housing, it may be a better idea
    to bet on land.
The limitations

   This analysis assumes no arbitrage.
    But this is not so for RE transactions.
   Thus, we would expect deviations from
    the equality, r = rf + w –  × (rm + w) +
     – g + .
   These deviations may last for a long
    time (why?), but should not last forever
The impact of housing market
on rental market
   In 2007, “apartment building have been one of the
    few bright spots in the real estate industry as
    people forced out of the home-buying market by
    foreclosures or the credit crunch have turned to
   “But now the specter of job losses is beginning to
    spread the gloom into that sector as well. As
    would-be renters are doubling up in apartments or
    moving in with friends and families, rent growth and
    occupancy rates are beginning to fall in many
   Source: WSJ, Aug. 20, 2008.
Group assignment

   Please study the housing market in
    Burlington and neighboring cities/villages.
   Please use the analysis framework to
    analyze the local housing market and
    answer the following question: is this a good
    time to buy a house here?
   Please submit your group report in a week.

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