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Does Sacramento's housing "Existing Need" exist? Nope!

I am referring to the Department of Community and Housing Development (HCD) Residential Housing Need Allocation (RHNA). Sacramento has mandated the State build 2.5 million housing units between 2023 and 2031. This is referred to as the 6th RHNA cycle. For more background information on the subject, I recommend Amy Kalish excellent article: RHNA basics

The 6th RHNA cycle housing units target of 2.5 million is over twice as large as the 5th RHNA cycle target of 1.2 million that covered the previous eight-year period (2015 - 2022). This is not because of California's accelerating population growth. California's population has decelerated ever since the 5th RHNA cycle. And, it has declined since 2019.

Sacramento introduces the "Existing Need" concept

The difference between the 5th and 6th RHNA cycles is mainly due to the 6th RHNA cycle's introduction of "Existing Need." Independent of population change, Sacramento believes California is severely overcrowded due to a housing supply shortage. And, that explains California's exorbitant housing prices. Sacramento believes that by adding about 1.25 million housing units solely for Existing Need in addition to the other 1.25 million for Projected Need, California housing prices would decline to more reasonable levels.

Houses are not potatoes

Sacramento's logic relies on a deterministic economics framework that works reasonably well for "commodities." The latter are goods that are standardized. Such commodities include just about any retail product. It includes food items, energy fuel, minerals, etc. Many commodities can be readily traded at the CME Group. When you increase the supply of a commodity, its price typically decreases.

Unfortunately, housing is not a commodity. Housing is subject to location-specific characteristics that render the Supply and Demand framework uninformative. A house in California is a different good than a house in Arkansas. That's even the case if both houses have identical specifications (2,000 square feet; 3 bedrooms, etc.). The same is true for rentals.

Additionally, the housing market is a blind-retail-auction market. No one knows what other bidders are offering. Given that, it is not unlikely for the winning bidder-buyer to overpay. This phenomenon is known as the Winner's Curse. Wikipedia article on Winner's Curse

Empirical studies on increased housing supply vs prices or rents

In 2019, Xiaodi Li, a professor at NYU uncovered that a 10% increase in housing supply translates into a -1% decrease in rents within only a 500-foot radius in New York. See his paper attached at the end of this essay. This is an immaterial impact.

Let's take an example. You live in a town with 5,000 housing units. You then increase the housing supply by 10% or 500 units. Next, let's say the average rent is $2,000. After the 10% increase in housing supply, average rents would decrease by only - 1% to $1,980. And, that is only for apartments within a 500-foot radius of where the new apartment buildings are located. What this study does not tell you is that such an increase in housing supply, absent any population increase would result in an unsustainable 9 percentage points increase in vacancy rate. This is very much the situation in California (no population growth). See my earlier essays on this topic:

Sacramento mandates don't make any economic sense

Sacramento is creating an inevitable apartment Housing Bubble

In 2023, Australian social scientists uncovered that higher density (increase in housing supply) translated into higher housing prices in Brisbane, Australia. See their paper attached at the end of this essay.

In 2023, Patrick Condon, a professor, researched that higher density led to a huge increase in home prices in Vancouver, Canada. See the related article at the end of this essay.

I thank Amy Kalish and Marc Verville for uncovering these informative studies.

In addition, Sharon Rushton has written a seminal article covering this topic. She goes over Patrick Condon's study in great detail. And, she mentioned several other studies on the subject. Sharon Rushton's essay on housing density vs prices

As reviewed above, only Li uncovers a marginal reduction in rents due to an increase in housing supply. All the other social scientists uncover an opposite effect that an increase in housing supply through densification results in higher home prices and rents because land becomes a lot more valuable than prior to the increase in housing supply.

Is there evidence of California being overcrowded?

The Sacramento pro-growth advocates think so by focusing on California's average household size being higher than the national average at about 2.9 vs. 2.5, respectively. This argument does not hold up once we better understand the data.

First, over the past few years, California's household size has rapidly mean-reverted downward towards the US household size. As shown in the graph below, California's household size has declined from 2.96 in 2016 to 2.77 in 2023. Based on current demographic trends (aging population, slowing migration), we can expect the California household size to continue declining.

Avg-HH-California.pngNext, looking at data over the past 4 years confirms that household size has no downward impact on home prices.


Data source: Housing unit, population, average household size -> Department of Finance. Home price, Zillow Zestimate Index.

The table above shows that since 2019:

The above data directly contradicts the notion that:

  1. An increase in housing supply would lower home prices; and
  2. A decrease in household size would be associated with lower home prices.

The above readily nullifies RHNA 6th cycle Existing Need concept.

Studying the relationship between household size and home price

The pro-growth advocates think that high home prices are causally related to high household size (the overcrowding argument). That's the foundation of the Existing Need political construct.

The two scatter plots below visualize the relationship between household size on the X-axis and home price on the Y-axis for all the States in the Union. We scale home prices by focusing on the ratio of median home prices divided by median household income.

The plot on the left just shows the data. The one on the right defines two zones of interest (blue and green rectangles).


Data source: Median household Income, Census 2022. Median State home price, Zillow Zestimate index 2022. Average household size, US Census.

The scatter plot on the right shows that there is no relationship between home prices and household size for the vast majority of the States (blue rectangle).

When we focus on the States (AK, TX) with nearly identical household sizes as California, we notice that they have dramatically lower home prices than California (green rectangle).

All of the above suggests that California's relatively high household size is not a function of high home prices or a valid marker of overcrowdedness justifying Sacramento's Existing Need construct.

Next, look at home prices over time (as specified Median home price/Median household income). Here we included the 10 states with the highest household size (per Sacramento, these would be the 10 most overcrowded). California ranked 5th among those 10 states.


Data source: same as the earlier graph

The graph above shows that since 2000, California's home prices, as defined, have been by far the highest one on the Mainland among these 10 States. Only Hawaii has higher home prices.

When focusing on California and Hawaii, one can see that California fully experienced the Housing Bubble (price boom up to 2005, and ensuing crash over the next 5 years). Hawaii home prices did not bubble up. Instead, they remained very high.

The facet grid below gives a clearer look at each of the 10 States' home price trends.

Existing-time-series-2.pngData source: same as the earlier graph

The facet grid above reveals that the majority of the States' home prices are revisiting or even exceeding their peak levels during the Housing Bubble. This could raise concerns. However, the financial leverage within the banking system and among mortgage borrowers is not nearly as high as it was during the Housing Bubble.

Let's revisit the three States that have currently nearly identical household sizes close to 2.8. These are California, Arkansas, and Texas. As shown below, California home prices, as specified, are consistently way higher than in the other two States.


Data source: same as the earlier graph

Notice that both Arkansas and Texas home prices, as specified, are now way higher than during the Housing Bubble. Meanwhile, California's home prices have not quite reached the peak experienced during the Bubble.

Affordability analysis

Home affordability is driven by two factors. Home prices and mortgage rates.

Since early 2022, mortgage rates have risen from 3.00% to above 7.00%, as shown in the graph below.


Next, let's explore what is the impact of home prices, as specified, and mortgage rates on affordability. I'll capture affordability as a debt service ratio or the ratio of mortgage payments divided by household income.

The model output (shown below) captures a wide range of home prices associated with a multiple of household income ranging from 3 to 9 times. Mortgage rates range from 3.00% to 8.00%. To keep things simple, the model assumes 100% financing of the home and a 360-month amortization. (To this day an FHA program still finances 97% of a home value, not far from my 100% assumption). The percentages within the table represent the resulting debt service ratio associated with the depicted home prices and mortgage rates.


Within the table above, the area in red denotes when a home becomes unaffordable. It would be challenging to get a mortgage with a loan-to-value ratio of 100% and a debt service ratio greater than 40%.

So, how can a State median home prices reach multiples of 7 times or greater of the State median income? There are two explanations for that.

The first one is that the median household income for homeowners can be much higher than the one for renters.

Below I use an example relevant for California. As input, I use an overall median household income of $85,300 and a homeownership rate of 55%. These are the current figures for California. Next, I explore various renters' median household income ranging from $40,000 to $80,000. Finally, given those inputs, I calculate the resulting homeowners' median household income; so that the overall median household income still remains $85,300.


As the table above shows, the homeowners' household income could be a multiple higher than for renters.

In the tables below, I use the respective household incomes to calculate the resulting home price multiples for homeowners vs renters. Meanwhile, the overall median home price remains constant at 8 times the median household income.


As shown above, what are deemed to be very expensive homes could be more affordable for homeowners vs renters.

The second factor that can explain high home price to income multiples is that a large share of homes is purchased with a substantial capital contribution towards a downpayment. Marc Verville, using FHA data, uncovered that family-assisted downpayment was associated with over 30% of mortgage loans originated in the San Francisco Bay Area in 2022. He also advances that there is a rising percentage of institutional investors purchasing homes in California. Also, many Californians have accumulated much wealth by working in the high-tech sector. And, they have reinvested their capital into cash purchases of homes. Thus, California home prices have not been driven upward so much by wages but more by capital accumulated by individuals or institutions.

Are homes priced at 8 times household income inordinately expensive when looking at top international real estate markets?

The table below indicates that this is not the case.


Five California cities including Los Angeles, San Francisco, San Diego, San Jose, and Riverside make it in the top 20. Except for Riverside, they all have multiples much above 8.

Notice that Australia also has 5 cities on that list. Two out of the three have multiples above 12! And, Australia's population is 30% smaller than California. Its GDP per capita is lower. Australia does not have worldwide beating hi-tech companies as California does. All those factors suggest that Australian cities should be cheaper than Californian ones. They are not.

Among the top 20, you see many cities belonging to other countries including China, Canada, the UK, and New Zealand.

When you factor in the location-specific assets of California cities (environment, climate, leading universities, leading hi-tech sector, etc.), California cities' high home prices are not out of line with their foreign city counterparts.


The Existing Need concept does not hold up to closer scrutiny and understanding of the relevant data. Increasing housing supply through densification is unlikely to lower home prices or rents.

Based on current demographic trends, California's household size is progressively aligning with the ones from other states. And, states with a very similar household size as California have home prices that are way cheaper than California. Thus, California's current average household size is not a marker of overcrowdedness and associated high home prices.

This analysis confirms that Existing Need is a political construct that is detached from demographic and economic trends.


housing, demographics, politics