A brief note on (1) cause and effect and (2) multiple regression analysis: for Housing Studies students

A brief note on (1) cause and effect and (2) multiple regression analysis: for Housing Studies students (information from perplexity.ai)

My question 1: "Regarding the topic cause and effect, briefly explain what is a necessary cause and what is a sufficient cause in the context of Housing Studies research."

The reply: "A necessary cause in Housing Studies research is a condition that must be present for a particular housing outcome or effect to occur, though on its own it might not be enough to produce the effect; the outcome cannot happen in its absence. For example, access to housing might be a necessary cause for improved health, because without any housing, health improvements linked to housing are impossible.​

A sufficient cause in Housing Studies research refers to a condition or set of conditions that, if present, will guarantee the occurrence of the housing outcome or effect. For instance, if sufficient affordable housing is provided for a population, it is enough by itself to ensure that nobody experiences housing-related poverty, regardless of other factors."

My question 2: "What are the limitations of multiple regression analysis to study cause and effect in the context of Housing Studies?"

The reply: "Multiple regression analysis in Housing Studies can help identify statistical relationships among variables but has notable limitations in studying cause and effect. While useful for predicting outcomes like housing prices or affordability, these limitations present significant challenges.​

Key Limitations

• Cannot Prove True Causality
  Multiple regression analysis mainly establishes associations, not causation. Effects seen in models may simply be correlations—external factors or omitted variables can confound the results, leading to spurious findings that do not reflect true cause and effect relationships.​

• Omitted Variable Bias and Endogeneity
  Housing phenomena are influenced by many interconnected factors. If important variables are missing (omitted variable bias), or if variables are correlated with unmeasured causes (endogeneity), regression results may be misleading. For example, the relationship between housing costs and building features (like elevators) can be confounded by factors such as building location or project design, which are not included in the model.​

• Multicollinearity
  In housing research, many independent variables are highly correlated (e.g., housing size and number of bedrooms), which can make it difficult for the model to disentangle the independent effects of each variable. This reduces the reliability of estimated coefficients and complicates interpretation for cause and effect.​

• Sample Size and Data Quality
  Small samples or incomplete data can distort findings and generalizability. In housing studies, richness and variability of data across projects, locations, and timeframes are often limited, weakening the robustness of regression-based conclusions about cause and effect.​

• Model Specification and Interpretation Challenges
  Adding more variables does not necessarily create better models; the choice of what to include, how to measure, and how to interpret relationships is subject to researcher bias and technical limitations. Differences in project types, market contexts, and measurement practices can undermine causal claims.​

These limitations mean that multiple regression can signal where relationships exist but cannot by itself establish that a housing-related factor "causes" an effect without additional research design elements, such as experiments or longitudinal studies."

A collection of blog notes on using chatgpt for research purpose.