Transcript Housing Economics
Hedonic Modeling Mats Wilhelmsson Center for Banking and Finance (Cefin)
Lecture
• Tuesday 15/2 – Wilhelmsson, Mats (2008). House Price Depreciation Rates and Level of Maintenance. Journal of Housing Economics. Vol.17(1), 88-101.
– Kryvobokow, Mark and Wilhelmsson, Mats (2007). Analyzing location attributes with hedonic model for apartment prices in Donetsk, Ukraine. International Journal of Strategic Property Management, Vol.11, 157-178.
– Song, Han-Suck and Wilhelmsson, Mats (2010). Improved price index for condominums.
Price Variation in Space
• • • Why do housing prices vary in space?
– That is to say, what can explain the price variation when we are using cross-sectional data?
Attributes – Property specific • Size, quality, age – Neighborhood specific • Positive and negative externalities • Segmented market The relationship between housing price and housing attributes is estimated with the so-called hedonic regression 3
The Hedonic Regression
• • Relating price to attributes of the goods Housing, cars, electronics, wine….
• • • The hedonic regression is based on the hedonic value model where the property value is a function of property attributes.
Hedonic regression controls for differences across individual properties by modeling the value effects of those differences.
That is, we estimate an implicit (hedonic) price of the attribute – Haas (1922), Court (1937) and Rosen (1974) 4 First Hedonic Theory
The Hedonic Price Equation Price
P
(
Z
)
1
F
2
O
3
T
• Attributes (Z) – Property specific attributes (F) • Apartment specific attributes – Neighborhood specific attributes (O) – Time specific attributes (T) 5
The Hedonic Methodology max
s
.
t
.
I u(y,
P
y
z
i
)
y
P
(
z i
)
FOC
P
z i
p z i
u
u
z i
y
MU z i MU y
i
Estimated parameters in the hedonic regression is equal to the marginal willingness to pay, that is, the hedonic price is equal to how much the individual is willing to give up of other goods to get attribute z.
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The Hedonic Methodology
• • The first stage: The second stage: • Estimation of: – Price elasticity – Income elasticity P z Estimation of P(Z)
Z
g
( ,
I
,...)
i P z i
?
?
7 z
When it is used?
• Valuation/appraisal • Estimation of willingness to pay (WTP) – Neighborhood specific attributes •
Golf courses, Power lines, Sea view, city plans
•
Proximity to roads, airports etc etc
– Property specific attributes •
Quality, depreciation, size
• Time specific attributes •
Index construction
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Article 1: House Price Depreciation Rates and Level of Maintenance
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2.
3.
4.
5.
6.
Introduction – The introduction states the basic objectives of the study and explains why it is important.
Literature review – All papers, even if they are relatively short, should contain a review of relevant literature.
Theoretical model and Method – In this section, you describe the general approach to answering the question you have posed.
Descriptive statistics – You should always have a section carefully describes the data used.
Econometric analysis – The results section should include your estimates of any models formulated in the models section.
– Summarize and conclusion This could be a short section that summarizes what you have learned.
Introduction
• • • Depreciation may bias the estimation of CPI, appraisals, tax assessment Why does a property depreciate over time?
– Physical deterioration – Functional obsolescence • Technological changes – External obsolescence • Changes in neighborhood My main objective is to estimate different house price depreciation rates depending on the level of maintenance.
– Keeping physical deterioration constant
Literature Review
• • • • • • • • Malpezzi et al (1987) - literature review, estimate deprecation rates and how it differs across markets (0.9% to 0.3%).
Shilling et al (1991) - tenure status, lower depreciation rates in owner occupied properties Rubin (1993) - why negative age effect – taste for newer houses Goodman and Thibodeau (1995) order effect - age induced heteroskedasticity – depreciation is non-linear, important to include the age effect as a second Knight and Sirmans (1996) – maintenance, similar to my study. (0.9% to 1.9% depending on maintenance) Clapp and Giaccotto (1998) - over time, across space Knight et al (2000) – No effect. My argument is that their assumptions are unrealistic.
Smith (2004) - across sub-markets (0,5%-7% depending on area)
Method
• • •
Hedonic Spatial Econometrics (I will come to that) Specification:
ln(
Y
)
X
A
A
2
sq
AM
M
•
Implicit price:
Y
A
Y
sq
2
AY
M MY
Data - Descriptive Analysis
Price Living area Other area Lot size Rooms* Quality Sea view* Age Distance Sauna* Heating* Cabel-tv* Garage* Fireplace* Inside maintenance* Outside maintenance* Drainage Electricity Kitchen Laundry Road traffic* Number of observations Unit SEK Square meters Square meters Square meters Number Index Binary Year Meters from CBD Binary Binary Binary Binary Binary Binary Binary Binary Binary Binary Binary Binary Average 2545026 118 58 724 5.03 27.6 0.05 51 8754 0.35 0.20 0.30 0.62 0.63 0.79 0.50 0.38 0.56 0.68 0.63 0.28 640 Standard deviation 1215467 43 33 266 1.3 5.9 18 2694
Properties in need of maintenance(%)
Age group 0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 All Outdoor 16 36 39 38 41 63 53 61 50 Indoor 37 52 78 79 83 86 84 80 79 Electricity 0 20 28 43 53 72 63 67 56 Laundry 21 48 64 57 55 70 61 73 63 Drainage 0 8 19 36 41 52 39 48 38 Kitchen 21 72 67 64 64 73 73 71 68
Property age and location from CBD
80 60 40 20 0 0-4999 7000-8999 5000-6999 11000-12999 9000-10999 15000-16999 13000-14999 Distance (meter)
Econometric Analysis
7 Age 8 Age square 18 IM 19 OM 20 AIM 21 AOM Adjusted R 2 Moran's I Model 1 -0.01178 (-3.72) 0.00018 (5.02) 0.604 4.16 Model 2 -0.01031 (-3.41) 0.00016 (4.70) -0.05429 (-2.08) -0.10713 (-5.23) 0.669 2.94 Model 3 Model 4 Model 5 -0.00951 -0.00714 -0.00800 (-2.91) 0.00011 (3.04) (-2.30) 0.00009 (2.47) (-2.59) 0.00011 (3.28) -0.07818 (-3.15) -0.09442 (-4.78) 0.675 .07 0.724 -.21 -0.00144 (-3.03) -0.00181 (-5.04) 0.725 -0.15
15% 10% 5% 0% -5% 0 -10% -15% -20% -25%
Depreciation effect
Age effect IM OM IM+OM 10 20 30 10 40 50 60 23
Regression results
Age Age square AIM AOM AElectricity AKitchen ALaundry ADrainage Adjusted R 2 Model A Coefficient -0.00807 0.00012 -0.00144 -0.00181 0.725 Model B Model C t-value Coefficient t-value Coefficient t-value -2.61 3.30 -0.00813 0.00011 -2.64 3.28 -0.00773 0.00012 -2.51 3.50 -3.03 -5.02 -0.00046 -0.00109 -1.17 -2.42 -0.00065 -0.00139 0.00017 -0.00137 0.725 -1.66 -3.14 0.42 -3.74 0.00023 -0.00100 -0.00079 -0.00142 0.733 0.58 -2.67 -1.56 -3.84
Conclusions
The main contribution in this paper is that the analysis relates depreciation rates to the level of maintenance.
The results show that the depreciation rates are significantly lower for a maintained property compared to a non maintained property.
The depreciation rate is estimated to be 0.77 percent per year for a well-maintained property and 1.10 percent for a property that is not renovated in- or outdoors year 1. In year 20 the annually depreciation rate is estimated to be 0.42 percent respectively 0.84 percent.
Article 2: Analyzing location attributes with hedonic model for apartment prices in Donetsk, Ukraine • • • • • • Hedonic Apartments Values (instead of prices) Sub-centers – Monocentric vs. polycentric Positive and negative externalities Price gradient – different in different directions
Household Consumption Pattern of Housing Attributes
• • First stage: House prices as a function of housing attributes hedonic prices Second stage: hedonic prices as a function of quantity, income and other socioeconomic characteristics.
• However, problem with the second stage – Simultaneous decision – hedonic price and quantity – Two ways estimating it • Assume utility function • Multiple markets