Part 3 of 3
In the previous installments of this series, the concept of a
global property price index was explored using data from Numbeo. Different
methodologies for creating custom indexes were delved into, including weighted
indexing and effect coding, to challenge the limitations of traditional generic
rankings. In this final piece, the focus shifts to regression modeling to
construct a competing index and re-rank countries based on its predictions.
This approach aims to provide a more nuanced understanding of housing
affordability, moving beyond simple averages and considering the interplay
between various factors such as income, rental yields, and mortgage burdens. By
leveraging this data-driven approach, international consultants and analysts
can empower their retiree and investor clients with the knowledge and insights to make informed decisions about global relocation and investment
strategies.
Regression Model
The Dependent Variable: A regression model aims to predict or explain the
relationship between one or more independent variables and a dependent
variable. The equally weighted composite index (Part 1 of the series) serves as
the dependent variable in this regression model. As discussed, normalizing the
component indexes before creating the composite index also ensures that each
component contributes equally to the overall measure. This standardization is
crucial for meaningful comparisons across countries, as the scales of different
component indexes can vary significantly.
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Analysis
of the Regression Output
Overall Fit:
R-squared: 0.9981 indicates that the model
explains a high proportion of the variance in the weighted index, suggesting
that the four independent variables are strong predictors of housing
affordability.
Adjusted
R-squared:
0.9066, while high, is lower than the R-squared, suggesting that some of the
model's explanatory power might be due to chance.
Coefficient Interpretation:
International consultants and
analysts can interpret the four regression coefficients in the following ways
to provide insights to their expat retiree and foreign investor clients:
Property Price to Income:
The coefficient of 0.01423 suggests that for every one-unit increase in the
Property Price to Income ratio, the weighted index is estimated to increase by
0.01423 units. This indicates how property prices relative to income impacts
the overall affordability index.
Gross Rental Yield:
With a coefficient of 0.01012, this variable has a significant impact on the
weighted index. A one-unit increase in Gross Rental Yield is associated with an
estimated 0.01012 unit increase in the overall index. This suggests that higher
rental yields contribute positively to the overall attractiveness of a housing
market.
Property Price to Rent:
The coefficient of 0.00314 implies that a one-unit increase in the Property
Price to Rent ratio leads to a 0.00314 unit increase in the weighted index.
This variable indicates how the price of property relative to rental income
influences the overall index.
Mortgage to Income:
The coefficient of 0.00146 suggests that as the Mortgage to Income ratio
increases by one unit, the overall index is estimated to increase by 0.00146
units. This variable provides insights into the impact of mortgage burden on
the overall affordability and attractiveness of a housing market.
Significance
of Coefficients:
All four independent variables have
statistically significant coefficients at the 0.05 level (p-values < 0.05),
indicating that they all contribute meaningfully to explaining the variation in
the weighted index.
The regression output demonstrates that the regression model
created to generate a global property price index is highly effective. It
reveals strong explanatory power, significant relationships between the
independent variables and the dependent variable, and reliable predictions of
the weighted index.
This regression model provides a valuable framework for
international consultants to analyze housing affordability for expat retirees
and foreign investors. By understanding the relationships between key factors
and the weighted index, consultants can provide more informed and tailored
advice. Therefore, international consultants and analysts can confidently
develop similar models to provide valuable insights to their clients regarding
housing market evaluations, investment decisions, and strategic planning in the
global real estate landscape.
Gross Rental Yield vs. Property Price
A higher Gross Rental Yield is generally inversely related to
higher property prices.
Gross Rental Yield is calculated as the annual rental income
generated by a property divided by its market value, expressed as a percentage.
A higher rental yield means that the property is generating a higher rental
income relative to its value.
In a market with high rental yields, this typically indicates that
properties are relatively more affordable compared to the rental income they
generate. This could be due to lower property prices or higher rental incomes.
Conversely, in markets where property prices are high, the rental
yield tends to be lower as the property value increases while the rental income
remains relatively stable. This means that investors may have to pay more for a
property relative to the rental income it generates, resulting in a lower
rental yield.
Therefore, a higher Gross Rental Yield is usually associated with
lower property prices and vice versa. Investors often look for a balance
between rental yield and property prices to identify opportunities for
potential investment returns.
How
to Use the Regression Model
International consultants and analysts can use
this custom method of creating a global property price index through regression
modeling to provide valuable insights to their expat retiree and foreign
investor clients in the following ways:
1. Understanding Housing Markets: By creating a custom index based on
specific factors such as property price to income, gross rental yield, property
price to rent, and mortgage to income, consultants can offer a more tailored
evaluation of different housing markets. This allows for a more nuanced
understanding of each market's affordability, rental potential, and financial
burdens.
2. Predicting Market Trends: The model can help predict how
changes in key factors like income, rental yields, and mortgage rates might
impact housing affordability in different markets, allowing consultants to
advise clients on potential investment and relocation strategies based on
anticipated market shifts.
3. Tailoring Investment Strategies: The model can help tailor investment
strategies based on client preferences and risk tolerance. For example, a
client seeking high rental yields could benefit from markets with a strong
positive coefficient for "Gross Rental Yield."
4. Evaluating Market Risks: By examining the model's residuals,
consultants can identify markets where the actual weighted index (the dependent
variable) deviates significantly from the predicted value. These markets might
present higher risks or offer unique investment opportunities.
5. Understanding Cost of Living
Differences: The
regression index provides a more tailored and data-driven approach to
evaluating differences in the cost of living of foreign countries. By incorporating
specific variables that influence property market dynamics, the regression
index offers a more nuanced perspective than traditional generic rankings.
In summary, the regression modeling approach
to creating a global property price index offers international consultants and
analysts a robust tool for analyzing and comparing housing markets worldwide. Since
this index takes into account factors such as property price to income, gross
rental yield, property price to rent, and mortgage to income, it allows for a
more comprehensive assessment of affordability and investment potential in each
country.
By leveraging the insights from the regression
output, consultants can provide data-driven recommendations to expat retiree
and foreign investor clients, helping them make informed decisions on where to
live, work, and invest in the global real estate market.
Analyzing
the Regression Index and Re-Ranking
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The challenger regression index and the
resulting re-ranking of the countries based on the regression index provide
valuable insights for international consultants and analysts, helping their
retiree and investor clients understand the cost of living differences in
foreign countries.
This approach can be utilized to analyze the
movements in the new ranking vis-à-vis the original generic ranking:
· Overall
Pattern: The regression
index generally follows the original weighted index but with some notable
shifts in rankings, suggesting that the regression model captures the overall
trend of housing affordability but introduces adjustments based on the
relationship between the four independent variables and the weighted index.
· Mexico and New Zealand Rising: Mexico and New Zealand have risen in
the new ranking based on the regression index, indicating that these countries
are performing relatively better in terms of the factors influencing the
property price indexes (such as property price to income, rental yield, property
price to rent, and mortgage to income) compared to the generic ranking.
· Germany and Switzerland Declining: Conversely, Germany and Switzerland
have declined in the new ranking, suggesting that these countries may not be as
favorable regarding the specific factors considered in the regression model
compared to the overall generic ranking.
· Brazil
and Chile:
These countries maintain their top positions, indicating that their high
affordability is robust across different factors the model considers.
· UAE
and United States:
These countries, with high rental yields but also high property prices, show a
slight improvement in their ranking, suggesting that the model recognizes the
positive impact of rental yields on affordability, even in markets with high
property prices.
In summary, the challenger regression index
and the resulting re-ranking provide a powerful tool for international
consultants and analysts to offer customized insights and recommendations to
their retiree and investor clients regarding the cost of living differences in
foreign countries. By incorporating specific variables and comparing the new
ranking with the original generic ranking, consultants can provide valuable
guidance for strategic decision-making and investment planning in the global
real estate market.
Putting
it All Together
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Overall Observations:
· Consistency at Extremes:
Chile and Brazil consistently rank high across all methods, suggesting they
offer relatively favorable real estate markets from a global perspective.
Conversely, the USA and UAE consistently rank low, indicating they may be less
attractive in terms of affordability, rental yields, or mortgage burdens.
· Movement in the Middle:
The rankings of most countries fluctuate among the three methods, highlighting
the sensitivity of the rankings to the specific weighting and assumptions used
in each approach.
Method-Specific Insights:
· Weighted Indexing Method (Equal Weighting):
This method assigns equal importance to all factors considered in the index.
The rankings here reflect a balanced view of various aspects of the real estate
market.
· Effect Coding Method (Average
Deviation): By using average deviation, this method emphasizes how each
country's performance deviates from the global average. Countries with
significantly higher or lower values than the average will be ranked more
extreme.
· Regression Modeling Method (Multiple
Regression Analysis): This approach attempts to
identify the most influential factors contributing to real estate market
performance and assigns weights accordingly. The rankings here reflect a more
nuanced understanding of the complex relationships between different variables.
Country-Specific Analysis:
· Italy and Spain:
Their consistent ranking across all methods suggests that their real estate
markets have a relatively stable and predictable performance.
· Japan and France:
Their slight decline in rankings across methods might indicate that their
relative attractiveness has decreased compared to other countries when
considering factors beyond the original index.
· Remaining Countries:
The significant movement in their rankings highlights the sensitivity of the
results to the chosen methodology, suggesting that the attractiveness of their
real estate markets can be interpreted differently depending on the specific
factors being emphasized.
Overall, the analysis reveals the
complexity of ranking global real estate markets. The chosen methodology
significantly influences the results, highlighting the need for careful
consideration and interpretation.
Series
Conclusion
The journey through this series has
underscored the critical role of custom indexes and re-ranking in navigating
the complex global real estate market. By moving beyond generic rankings and
embracing data-driven approaches like weighted indexing, effect coding, and
regression modeling, international consultants and analysts can gain a deeper
understanding of housing affordability across different countries. This nuanced
perspective empowers them to provide tailored solutions that address the unique
needs and preferences of their retiree and investor clients.
By developing composite indexes encapsulating
meaningful components like affordability, rental yields, and mortgage burdens,
consultants can offer a nuanced understanding of market conditions and trends.
The ability to tailor solutions based on custom indexes enables consultants to
provide personalized recommendations that align with their client's unique
preferences and goals.
Whether it's identifying undervalued markets,
tailoring investment strategies, or assessing potential risks, the insights
gleaned from custom indexes and re-ranking can significantly enhance the value
of the services offered by these professionals. As the world becomes
increasingly interconnected and global mobility continues to rise, the ability
to analyze and interpret data innovatively will be paramount for those
navigating the complexities of the international real estate market.
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