1. Discussion: What drives metropolitan house prices in California?
Dr Kirdan Lees
Tuesday, December 9, 2017
Sense Partners

2. Structure comments around three areas
Brief outline of what the paper does
How to improve the paper
Make a case for using this type of model

3. What the paper does

4. What the paper does
Dynamic Factor model to analyse Californian house prices
Sets to one side forecasting for structural questions :
the impact of regional vs local housing shocks
the role of credit vs monetary policy shocks
impact on house prices of regional demand and inflation
Advocates specific identification scheme to identify shocks

5. Approach
Aggregate US time series
Californian time series
𝑍 𝑡 𝐶 𝑡 𝐻 𝑡 𝑖 𝑡 = 𝜆 𝑍𝑍 𝜆 𝑍𝑖 𝜆 𝐶𝑍 𝜆 𝐶𝐶 𝜆 𝐶𝐻 𝜆 𝐶𝑖 𝜆 𝐻𝑍 𝜆 𝐻𝐶 𝜆 𝐻𝐻 𝜆 𝐻𝑖 𝑓 𝑡 𝑍 𝑓 𝑡 𝐶 𝑓 𝑡 𝐻 𝑖 𝑡 𝜉 𝑡 𝑍 𝜉 𝑡 𝐶 𝜉 𝑡 𝐻 0
𝑓 𝑡 𝑍 𝑓 𝑡 𝐶 𝑓 𝑡 𝐻 𝑖 𝑡 =Φ(𝐿) 𝑓 𝑡−1 𝑍 𝑓 𝑡−1 𝐶 𝑓 𝑡−1 𝐻 𝑖 𝑡 𝜉 𝑡 𝑍 𝜉 𝑡 𝐶 𝜉 𝑡 𝐻 0
Metropolitan house prices
Interest rates
Variables load on factors contemporaneously
US variables contemporaneously unrelated to Californian variables
US variables contemporaneously unrelated to Californian variables
Californian variables load contemporaneously on US series

6. Identification scheme
Shocks
Response at horizon 𝑗=1,…,𝐽 MP AS AD AC RS RD
Aggregate inflation
𝑍 𝜋 - + *
Aggregate output
𝑍 𝑦
Money aggregate
𝑍 𝑚
HY spread
𝑍 𝑠 ++
Defaults
𝑍 𝛿
Californian inflation
𝐶 𝜋 --
Californian output
𝐶 𝑦
Federal funds rate
𝑖 𝑡

7. Strengths/ weaknesses of approach
Pragmatic approach for structural lens across data
Regional and metropolitan angles useful
Weaknesses
Results hang on plausibility of identification scheme
Unclear how well the data supports the model
Unclear how to think about expectations

8. Key findings of the paper
Regional demand shocks help drive house prices in California
Little role for monetary policy shocks
Credit shocks appear important…
…and not just for house prices, loan quality and employment more generally
Regional supply shocks appear relatively unimportant…?

9. How to improve the paper

10. Suggested improvements for this paper
Are the results robust to mild changes in the identification structure, choice of input data and interest rate specification?
Tighter integration with literature, eg Stock and Watson (2008) who find a West-Coast housing market, not much of a regional impact and a significant change in volatility using consents
Clarifying what is true for California market vs other markets
Some consistency issues across sections where work is ongoing, eg response to Fed funds, interest rate equation etc.

11. Suggestions for paper with broader audience
Another literature (growing) thinking about the relative impact of (tight) urban regulation and geography (eg Glaeser and Gyourko , Saiz 2010, Hsieh and Moretti 2017)
A Dynamic Factor model approach with appropriate restrictions could help identify local housing supply shocks (?)
3. Connect land use regulation to economic structure:
- Do markets with tight geography load on factors differently?
- Does local land regulation exacerbate boom/bust periods?
4. Could quantify local spillovers from tightly regulated markets

12. Strong case for using approach

13. Case study: Reserve Bank of New Zealand
Macroprudential (and monetary policy) impact of Auckland housing market critical feature of the past five years
Tangly to introduce housing markets and macroprudential in a small open economy (eg Neroli Austin 2016)
Useful to incorporate for policymakers that want advice from a range of modelling approaches
Approach likely to shine some light on Auckland vs rest of New Zealand issues for macroprudential