Is it a good time to buy a house in San Francisco?

Real estate in SF is up 33% since its low in March 2009.  To someone living in the Bay Area thinking about buying a house that seems daunting. And it doesn’t help that the likes of Twitter, Dropbox, Snapchat and Facebook have crazy valuations.

This post is my attempt at getting confidence in these questions:

1)   How much appreciation can I expect if I have to buy now and decide to sell in 5 years?

2)   If we are in a mini tech bubble and the bubble pops, what will be the impact on housing prices?

Model basics

My regression model consisted of the following:

1)   Supply of new homes – Privately Owned Housing Starts Authorized by Building Permits: 1-Unit Structures for San Francisco-Oakland-Fremont, CA (MSA) SANF806BP1FHSA

2)   Nasdaq as an indicator of how technology was doing (my assumption here is that a lot of the growth and decline in employment/salaries in the valley in the bay area is driven by tech); appreciation in the Nasdaq also incorporates asset inflation

3)   Long term interest rates – 10 year US treasury note to reflect what’s happening in mortgage rates; one flaw is that I haven’t taken into account the risk premium associated with mortgages which is also changes.

These are used to priced the home price index for SF (~25 years of data). Here is a plot of all the variables indexed to 100.

Housing prices, interest rates, nasdaq, and supply growth over last 25 years
Housing prices, interest rates, nasdaq, and supply growth over last 25 years

One thing to note here is that when the tech bubble burst housing prices slid only 10%. So that answers the question about if there is a bubble in social media the impact on housing prices may not be severe necessarily.

Does the model work?

Kind of. The model outputs show that these variables explain about 50% of the variance in the housing market and its significant:

Regression model outputs: Housing prices regressed against supply, interest rates, nasdaq;
Regression model outputs: Housing prices regressed against supply, interest rates, nasdaq;

Where does it work and where does it not?

Times when predicted  actuals buy - fundamentals are on your side
Times when predicted < actual mean don’t buy. When predicted > actuals buy – fundamentals are on your side

Model correctly predicts that home prices should not have risen in 2004-08 era. This means that given interest rates, Nasdaq, and supply the market was valued higher than these fundamental indicators would predict. If you bought then you would be taking a risk that values would adjust back to what was suggested by these indicators and you could lose money.

So now we can develop a measure of how good of a time it is to buy a house: when the prediction is much lower than the Home Price Index, don’t buy prices could fall (the red areas in the graph above). When the prediction is much higher than the Home Prices Index, homes are undervalued. Time to buy (the green areas).

Testing this new iteration

Let’s test how accurate this would have been. Take a new variable which I will call Delta =  Predicted prices / Actual prices. So in times when Delta > 1, you are getting a good deal (green areas in the graph above).

Now we plot Delta versus the 5 year forward return on home prices (unlevered) if you had invested in a home at that time (see Figure below). For example if you bought in 1988-01-01 (the first point), Delta would be 1.15 so it would be good time to buy (Delta >1). So if you bought then the red line represents that if you bought in 1988  your home would have appreciated about 1.3x or 30%.

When Delta >1 historically home prices appreciated over 5 years (and vice versa)
When Delta >1 historically home prices appreciated over 5 years (and vice versa)

The correlation between the measure of Delta and the 5 year appreciation is 84% and further more whenever the 5 year home price appreciation has been negative ‘Delta’’ has been less than one. Thus I can feel good if I buy a home I can predict the 5 year appreciation based on the delta and as long as Delta is above 1 I won’t lose money.

Bottom line

At the end of the graph you will see delta (the blue line) is hovering around 1. Based on where the Nasdaq, 10 year rates, and supply is right now, buying now will not yield to you a big upside in 5 years. You could wait to buy when Delta is >1 but it could take 5-7 years. If you can’t wait that long,  find something you feel is a relatively good deal but be picky.


Update: Excel model incase you want to change / update.

Facebook – It is worth the $100B price tag?

With Facebook ready to make its Wall Street debut, its worth debating what the value of the social network is. By any mean the world’s biggest social network is an incredibly valuable platform for advertisers in terms of reach and engagement.

(If you haven’t been bombarded with the numbers already …)

900 million monthly users … 7 hours per month / user … 500 million mobile users … 3,600 very skilled developers and business staff working hard

But, is the it really worth $100B? The bulls will say the hard part is done – they’ve got the users and people spending time on the website. Let’s for a moment give Facebook full credit for that – the back of the envelope still suggests that the valuation is rich by comparison to other media properties. Here’s my quick calculation—I assume that Facebook was able to monetize each user-hour at the same rate as the average of Google, Yahoo, and AOL Media:

Revenue / User / Hour for major web properties for US Users

Brand Unique Audience (US) Time / user / month (hrs) Total Time (M of hours) US Revenue ($, M) Rev / User / Hour
Google + Youtube 302,392,000 3.40 520 $17,560 $2.81
Yahoo! 142,691,000 2.45 350 3,303 0.79
AOL Media Network 86,268,000 2.73 236 2,001 0.71
Average online properties $1.44
Facebook 152,763,000 7.15 1,092 18,819 1.44

Sources: Neilsen, Capital IQ

According to this data, for every hour a US user spends on these sites it makes the site $1.44 in revenue /hour (most of the revenue for these businesses is driven by advertising). If Facebook were able to monetize at that rate it would be able to generate $18.8B in revenue from the US alone, and using Google as a proxy, could generate $43B in worldwide sales. In general a fast growing company maybe valued at 2-4x its revenues yielding around $90-130B valuation for Facebook

Note: Facebook advertising is much “softer” today. Its less sales driven and much more engagement driven, which is harder to value for advertisers. Google for reference is valued at $165B, and though it commands lower engagement from users per month, because of the nature of search marketing is able to command a premium per hour of the user’s time.

But to get here, Facebook has a lot of ground to cover:

1) It needs to experiment with its revenue strategy to get from $4B in sales to $43B in sales – and hold the attention of big name advertisers like GM.

2) Its got to figure out the mobile space with a better app (just look at the abysmal ratings in Apple’s Appstore) and monetization platform for mobile before someone else does

Bottom line: No doubt Facebook will be hard to displace and is here to stay for a while, but from an investor’s point of view it seems richly valued. Only if Facebook was able to monetize each user hour like Google, AOL and Yahoo would it be valued in the range it is today, but that’s still a while away and there are risks along the way.