Excel and Ppt project

Housing

Housing • Cost of Housing & Affordability

– Bureau of Labor Statistics (BLS) Shelter Index • Consumer Price Index (CPI) subcategory of shelter costs.

• Conducted monthly

– National Association of Realtors (NAR) • Provides data on existing pending home sales, actual sales, price data to the

county level, and housing affordability indexes.

• Conducted monthly

– Qualifying Income (NAR)

– Proportion able to afford a median priced home (CAR).

• The Housing Bubble – Shiller Index revealed high price volatility

• 240% ↑ from 1997-2006 and 120% ↓ from 2006-2009 • Federal Housing Finance Administration was much less volatile • Explanation: Shiller was a more comprehensive measurement and included

sub-prime financed units.

– Housing data is often too broad in scope • Most data is at the metropolitan area or larger. • Limited neighborhood, city, and county analysis. • California and San Diego Association of Realtors provide more geographically

specific prices.

– Predicting the housing bubble was challenging • Housing prices change due to fundamental and speculative factors

– Fundamentals (less volatile): income, rental value, inflation, vacancies, demographics, etc.

– Speculative (highly volatile): buy low and sell high for a quick profit.

• Some researchers confused fundamental and speculative forces and failed to accurately predict the bubble.

Housing

• Homeownership Rates – Rates increased to an all time high of 69% in 2004 and racial gaps had

shrunk significantly. – Formula: (owner-occupied households) ÷ (owner & renter occupied households)

– Rates can increase due to: • Renters becoming owners • Renters consolidate (move back home, take in roommates, etc.).

– Important: When the numerator and denominator are simultaneously changing, quick conclusions should not be made.

Housing

• Quality of Housing – American Housing Survey

• Compiles data on housing size and quality, neighborhood characteristics, home financing, and recently moved households.

• Conducted biennially in odd-numbered years.

– Changes in housing prices may reflect quality changes. – Shiller and FHFA control for many price influential variables by looking at the same home over

time (lot size, square footage, neighborhood, schools, etc.) making adjustments upon each new sale.

– Downward skew in prices during housing bust resulted, in part, from increased short-sales and foreclosures. • Units failed to represent the typical home (Sample Bias)

• Geographical Units – Important for detailed geographic issues and data consistency across time. – “City”, “County”, “Rural Area” are often subjective and arbitrary. – Census defines

• “Urban” as any incorporated place with more that 50,000 residents and “Built Up” characteristics. • Census Blocks (11.5 million in U.S) • Census Tracts (65,000 in U.S.)

– Metropolitan Statistical Area: • Determined by the Office of Management and Budget (OMB) based on economically and socially linked

geographies. • 389 in the U.S as of 2018.

Housing

• Best Places to Live – Different studies use different variables (climate, crime, housing, culture,

education, income, wealth, public transportation, etc.)

– Different studies may weigh variables differently.

– Hedonic Pricing: analyzes price differences to impute a value for a qualitative variable. • How much more would the same house sell for in San Diego vs. El Centro.

• Challenge is to determine which factors are causing the price differences (climate, crime, school system, etc..)

Housing

• Homeless – Estimates suggest anywhere from 500,000 to 3 million homeless in the U.S.

– Lower estimates: point-in-time head counts. • Records people in shelters, transitional housing, and on the street.

• HUD reports 553,742 (0.17% of population) homeless people on one night in Jan. 2017

• Fails to consider length of homelessness.

– Overestimates chronic homelessness since some individuals are only temporarily homeless.

– Underestimates the number of people that have been homeless at some time in their life.

– Larger estimates: one year estimates.

• HUD reports 1.56 million people spent at least one night in a shelter from 2009-2010

– Underestimate; does not include those on the streets.

– Highest estimates: extrapolation • Point-in-time estimates ÷ population in poverty

• National Law Center on Homelessness and Poverty and the Urban Institute generate a range of 2.5-3.5 million based on their January 2015 report.

• Fails to consider that the proportion of those in poverty that are homeless may change over time.

Housing

• Segregation – Typically measured by census track demographic data, obscuring neighborhood

segregation.

– Dissimilarity Index

• The proportion of a group that would need to move in order to achieve perfect integration.

• 1970 to 2010 index suggests decreased dissimilarity (less segregation).

• May be due to movements of Asians and Hispanics rather that Blacks.

Housing