Topic 4: Regional Economics

1. Topic 4:Regional Economics

2. Part A:Measuring House Prices

3. Housing price movements unconditionally • Census data • Transaction/deed data (provided by government agencies or available via public records) • Household data (PSID, Survey of Consumer Finances, etc.) • Repeat sales indices • FHFA (Google it – government agency) • Case-Shiller • Zillow • CoreLogic U.S. Housing Data

4. House prices can increase either because the value of the land under the home increases or because the value of the structure increases. • o Is home more expensive because the underlying land is worth more or because the home has a fancy kitchen? • Often want to know the value of the land separate from the value of the structure. • New homes often are of higher quality than existing homes. • Repeat sales indices try to difference out “structure” fixed effects – isolating the effect of changing land prices. • o Assumes structure remains constant (hard to deal with home improvements). Repeat Sales vs. Unconditional Data

5. FHFA – Federal Housing Finance Agency • Government agencies that oversee Fannie Mae and Freddie Mac • Uses the stated transaction price from Fannie and Freddie mortgages to compute a repeat sales index. (The price is the actual transaction price and comes directly from the mortgage document). • Includes all properties which are financed via a conventional mortgage (single family homes, condos, town homes, etc.) • Excludes all properties financed with other types of mortgages (sub prime, jumbos, etc.) • Nationally representative – creates separate indices for all 50 states and a large amount of metro areas. FHFA Repeat Sales Index

6. Developed by Karl Case and Bob Shiller • Uses the transaction price from deed records (obtained from public records) • Includes all properties regardless of type of financing (conventional, sub primes, jumbos, etc.) • Includes only single family homes (excludes condos, town homes, etc.) • Limited geographic coverage – detailed coverage from only 30 metro areas. Not nationally representative (no coverage at all from 13 states – limited coverage from other states) • Tries to account for the home improvements when creating repeat sales index (by down weighting properties that increase by a lot relative to others within an area). Case Shiller Repeat Sales Index

7. OFHEO vs. Case Shiller: National Index

8. OFHEO vs. Case Shiller: L.A. Index

9. OFHEO vs. Case Shiller: Denver Index

10. OFHEO vs. Case Shiller: Chicago Index

11. OFHEO vs. Case Shiller: New York Index

12. Aggregate indices are very different but MSA indices are nearly identical. • Does not appear to be the result of different coverage of properties included. • The difference has to do with the geographic coverage. • If using MSA variation, does not matter much what index is used. • If calibrating aggregate macro models, I would use OFHEO data instead of • Case-Shiller – I think it is more representative of the U.S. Conclusion: OFHEO vs. Case - Shiller

13. To assess long run trends in house prices (at low frequencies), there is nothing better than Census data. • Very detailed geographic data (national, state, metro area, zip code, census tract). • Goes back at least to the 1940 Census. • Have very good details on the structure (age of structure, number of rooms, etc.). • Can link to other Census data (income, demographics, etc.). • NOTE: The lower the level of geographic area in which house prices are measured (in all data sets), the more likely the data is either noisy or imputed. A Note on Census Data

14. Part B:Some More Data: Housing Cycles

15. Long run house price appreciation averages only 0-2% per year. • o These patterns are consistent across time • o These patterns are consistent across all levels of geographic aggregation (e.g., countries, state, cities) • Big booms are always followed by big busts • o These patterns are consistent across time • o These patterns are consistent across all levels of geographic aggregation (e.g., countries, state, cities) • Supply and demand determine housing prices • o Housing supply is very elastic in the long run (as demand goes up, we build more houses). Some Housing Facts

16. Average Annual Real Price Growth By US State (FHFA Data)

17. Average Annual Real Price Growth By US State (FHFA Data)

18. Average Annual Real Price Growth By US State (FHFA Data)

19. Inflation Adjusted Housing Price Growth in the U.S.

20. Housing Market: New York

21. Typical “Local” Cycle: California

22. Typical “Local” Cycle: Nevada

23. Average Annual Real Price Growth By OECD Country

24. Average Annual Real Price Growth By OECD Country

25. Country Cycles – The U.S. is Not Alone

26. Country Cycles – The U.S. is Not Alone

27. Country Cycles – The U.S. is Not Alone

28. Part C:Some Models of Spatial Equilibrium

29. Model Particulars (Baseline Model): The City • City is populated by N identical individuals. • City is represented by the real line such that each point on the line (i) is a different location: • : Measure of agents who live in i. • : Size of the house chosen by agents living in i. • (market clearing condition) • (maximum space in i is fixed and normalized to 1)

30. Household Preferences Static model:

31. Construction

32. Demand Side of Economy

33. Housing and Consumption Demand Functions

34. An Aside: Use of Cobb Douglas Preferences? Implication of Cobb Douglas Preferences:

35. Use CEX To Estimate Housing Income Elasticity Use individual level data from CEX to estimate “housing service” Engel curves and to estimate “housing service” (pseudo) demand systems. Sample: NBER CEX files 1980 - 2003 Use extracts put together for “Deconstructing Lifecycle Expenditure” and “Conspicuous Consumption and Race” Restrict sample to 25 to 55 year olds Estimate: (1) ln(ck) = α0 + α1 ln(tot. outlays) + β X + η (Engle Curve) (2) sharek = δ0 + δ1 ln(tot. outlays) + γ X + λ P + ν (Demand) * Use Individual Level Data * Instrument total outlays with current income, education, and occupation. * Total outlays include spending on durables and nondurables.

36. Engel Curve Results (CEX) Dependent Variable Coefficient S.E. log rent (renters) 0.93 0.014 log rent (owners) 0.84 0.001 log rent (all) 0.94 0.007 * Note: Rent for owners is “self reported” rental value of home Selection of renting/home ownership appears to be important

37. Engel Curve Results (CEX) Dependent Variable Coefficient S.E. log rent (renters) 0.93 0.014 log rent (owners) 0.84 0.001 log rent (all) 0.94 0.007 * Note: Rent for owners is “self reported” rental value of home Selection of renting/home ownership appears to be important Other Expenditure Categories log entertainment (all) 1.61 0.013 log food (all) 0.64 0.005 log clothing (all) 1.24 0.010 X controls include year dummies and one year age dummies

38. Demand System Results (CEX) Dependent Variable Coefficient S.E. rent share (renters, mean = 0.242) -0.030 0.003 rent share (owners, mean = 0.275) -0.050 0.002 rent share (all, mean = 0.263) -0.025 0.002 * Note: Rent share for owners is “self reported” rental value of home Selection of renting/home ownership appears to be important

39. Demand System Results (CEX) Dependent Variable Coefficient S.E. rent share (renters, mean = 0.242) -0.030 0.003 rent share (owners, mean = 0.275) -0.050 0.002 rent share (all, mean = 0.263) -0.025 0.002 * Note: Rent share for owners is “self reported” rental value of home Selection of renting/home ownership appears to be important Other Expenditure Categories entertainment share (all, mean = 0.033) 0.012 0.001 food share (all, mean = 0.182) -0.073 0.001 clothing share (all, mean = 0.062) 0.008 0.001 X controls include year dummies and one year age dummies

40. Households have to be indifferent across locations: Spatial Equilibrium

41. Equilibrium

42. hD(Y) ln(P) Graphical Equilibrium ln(κ) = ln(P*) ln(h*) ln(h)

43. hD(Y1) hD(Y) ln(P) Shock to Income ln(κ) = ln(P*) ln(h*) ln(h*1) ln(h)

44. hD(Y1) hD(Y) ln(P) Shock to Income (with adjustment costs to supply) ln(κ) = ln(P*) ln(h*) ln(h*1) ln(h)

45. Some Conclusions (Base Model) If supply is perfectly elastic in the long run (land is available and construction costs are fixed), then: Prices will be fixed in the long run Demand shocks will have no effect on prices in the long run. Short run amplification of prices could be do to adjustment costs. Model has “static” optimization. Similar results with dynamic optimization (and expectations – with some caveats) Notice – location – per se – is not important in this analysis. All locations are the same.

46. Equilibrium with Supply Constraints Suppose city (area broadly) is of fixed size (2*I). For illustration, lets index the middle of the city as (0). -I 0 I Lets pick I such that all space is filled in the city with Y = Y and r = r. 2I = N (h(i)*)

47. Comparative Statics What happens to equilibrium prices when there is a housing demand shock (Y increases or r falls). Focus on income shock. Suppose Y increases from Y to Y1. What happens to prices? With inelastic housing supply (I fixed), a 1% increase in income leads to a 1% increase in prices (given Cobb Douglas preferences)

48. ln(P1) Shock to Income With Supply Constraints ln(κ) = ln(P) hD(Y1) hD(Y) ln(h)=ln(h1) ln(h) The percentage change in income = the percentage change in price

49. Intermediate Case: Upward Sloping Supply ln(P1) ln(κ) = ln(P) hD(Y1) Cost of building in the city increases as “density” increases hD(Y) ln(h)=ln(h1) ln(h)

50. Implication of Supply Constraints (base model)? The correlation between income changes and house price changes should be smaller (potentially zero) in places where density is low (N h(i)* < 2I). The correlation between income changes and house price changes should be higher (potentially one) in places where density is high. Similar for any demand shocks (i.e., decline in real interest rates). Question: Can supply constraints explain the cross city differences in prices?