What Drives Housing Prices in Kochi? Remittances, Interest Rates, and the Limits of Macro Stability

 

1. Introduction and Motivation

Housing prices in Indian cities are often analysed through national-level indicators such as income growth, credit expansion, interest rates, and construction costs. While such approaches may work reasonably well for large metropolitan regions with diversified industrial and financial bases, they are less suited to states like Kerala, where housing demand is shaped by a distinctive political economy. High levels of out-migration, sustained remittance inflows, strong regulatory interventions, and episodic environmental shocks together create a housing market that does not conform neatly to all-India patterns.

Kochi provides a particularly revealing case for examining these dynamics. As Kerala’s primary urban and commercial centre, Kochi combines features of a service-oriented metropolitan economy with a housing market that is deeply influenced by external income flows. Residential investment decisions in the city are often linked not only to local employment conditions or borrowing costs, but also to remittances from the Gulf and other overseas destinations, regulatory actions affecting coastal and environmentally sensitive areas, and periodic disruptions such as floods or infrastructure-led redevelopment. Understanding what drives housing price movements in Kochi therefore requires a framework that goes beyond standard national housing models.

This study asks a focused empirical question: which macroeconomic indicators track housing price movements in Kochi most closely, and how stable are these relationships over time? In particular, it examines the relative roles of remittance inflows, monetary policy conditions, and construction-cost inflation, while also accounting for major contextual events that may alter market behaviour. Rather than treating housing prices as responding instantaneously to shocks, the analysis explicitly considers lagged effects and gradual adjustment, reflecting the slow-moving nature of real estate markets.

By grounding the analysis in Kochi-specific data and institutional context, the study aims to contribute to a more place-sensitive understanding of urban housing dynamics in India. The findings are intended to be relevant not only for academic debates on housing price formation, but also for planners, policymakers, and analysts seeking to interpret housing market signals in regions where migration and remittances play a central economic role.

2. Data and Variable Construction

The empirical analysis uses a quarterly dataset spanning 2013Q2 to 2025Q3, constructed by combining city-level housing price data for Kochi with national macroeconomic indicators and Kerala-specific contextual variables. The choice of variables reflects both data availability and the institutional features of Kerala’s housing market, where external income flows and policy interventions play an important role.

Housing Price Index

The dependent variable is the Kochi Housing Price Index (HPI) on an assessment-price basis. This series is sourced from the Reserve Bank of India’s city-level housing price statistics and reflects transaction values used for assessment purposes rather than advertised prices. The assessment-based index is preferred because it is less volatile and less susceptible to short-term speculative behaviour, making it more suitable for analysing medium- to long-run price movements. Quarterly observations are retained, and four-quarter moving averages are used in selected descriptive checks to smooth short-term noise.

Remittances

Kerala’s remittance inflows are captured using annual estimates published by the Centre for Development Studies (CDS) and the Kerala Migration Survey (KMS). These estimates represent total remittance inflows to the state and are widely regarded as the most authoritative source on migration-related income in Kerala. Given the absence of a quarterly remittance series, annual values are mapped onto quarterly observations and used with lags in the regression analysis. This approach reflects the fact that remittance-financed housing decisions typically operate with delays and are unlikely to respond instantaneously within a single quarter.

Monetary and Cost Variables

Monetary conditions are proxied using the RBI policy repo rate, converted to quarterly averages and introduced with multiple lags. Exploratory lag-correlation analysis suggests that housing prices in Kochi respond to changes in borrowing conditions with a delay of several quarters, consistent with the slow transmission of monetary policy to real estate markets.

Construction cost pressures are represented by the Wholesale Price Index (WPI), all items, also expressed as a quarterly average. While WPI is a national indicator and does not capture local construction market conditions perfectly, it serves as a broad proxy for input cost inflation in the absence of a consistent city-level construction cost index.

Event and Contextual Indicators

To account for major contextual disruptions, a set of indicator variables is constructed. These include a post-2018 flood dummy, capturing the period following the severe statewide flooding event, and a post-Maradu demolition dummy, reflecting the regulatory shock associated with the demolition of high-rise apartments in Kochi in early 2020. These indicators are specified as step variables rather than short-lived pulse dummies, on the assumption that such events influence expectations, risk perceptions, and regulatory behaviour over extended periods rather than within a single quarter.

Lag Structure and Data Handling

All explanatory variables are examined with alternative lag structures, and lag selection is guided by empirical correlation patterns and economic intuition. The final specifications emphasise parsimony and interpretability. Observations with missing values arising from lag construction are excluded on a listwise basis, resulting in a balanced estimation sample for each model.

3. Descriptive Patterns and Visual Evidence

Before turning to formal regression analysis, it is useful to examine the broad patterns in Kochi’s housing prices and their relationship with key macroeconomic indicators. Visual inspection of the data helps clarify the timing, persistence, and potential non-linearities in these relationships, while also motivating the lag structures adopted in the empirical models.

Housing Price Dynamics and Major Events

Figure 1: Kochi HPI (Assessment) with major event markers. Graph by Author.

Figure 1 plots the Kochi Housing Price Index over time, with markers indicating major contextual events such as the 2018 floods and the Maradu demolition episode. The series exhibits a clear upward trend over the full sample period, interrupted by periods of slower growth and mild corrections rather than sharp collapses. Notably, neither the flood event nor the demolition episode is associated with an abrupt, discrete break in the price level. Instead, the post-event periods are characterised by gradual adjustment, suggesting that housing prices in Kochi respond to shocks through changes in momentum rather than immediate re-pricing.

Co-movement with Monetary Conditions

Figure 2: Kochi HPI and RBI repo rate (standardised / z-scores) Graph by Author.

Figure 2 compares standardised values of the housing price index and the RBI policy repo rate. The two series display a broadly inverse relationship, with periods of monetary tightening followed by a deceleration in housing price growth. However, the response is clearly delayed, reinforcing the view that interest-rate changes affect housing markets with substantial lags. The weakening of this co-movement in recent years also hints at a possible decline in the strength of monetary transmission to the housing sector.

Co-movement with Remittances

Figure 3: Kochi HPI and Kerala remittances (standardised / z-scores) Graph by Author.

Figure 3 shows the standardised relationship between housing prices and Kerala’s remittance inflows. In contrast to the monetary variable, remittances display a strong and persistent co-movement with housing prices over much of the sample. Periods of rising remittance inflows coincide with sustained increases in housing prices, particularly prior to 2019. While the strength of this relationship appears to weaken in later years, the overall visual evidence points to remittances as a central demand-side driver of Kochi’s housing market.

A visual inspection of the series also suggests that housing prices, remittance inflows, and macro-financial variables exhibit persistent trends rather than rapid mean reversion. This raises standard concerns regarding non-stationarity in time-series regressions. Rather than mechanically differencing the data, which risks obscuring economically meaningful long-run relationships, the analysis proceeds by combining lag structures, trend-augmented specifications, and stability checks. This approach allows the empirical results to be interpreted as medium-run associations consistent with the slow adjustment characteristic of housing markets.

Implications for Model Specification

Taken together, these descriptive patterns motivate three key modelling choices. First, the absence of sharp breaks in the price series supports the use of level-based models rather than short-lived shock dummies. Second, the delayed response to monetary policy changes justifies the use of lagged interest-rate variables. Third, the strong visual alignment between remittances and housing prices underscores the importance of including remittance inflows as a core explanatory variable in the baseline regression framework.

4. Baseline Econometric Results

This section presents the baseline regression results examining the relationship between Kochi’s housing prices and key macroeconomic drivers. The empirical specification relates the assessment-based Kochi Housing Price Index to lagged remittance inflows, lagged monetary policy conditions, construction cost inflation, and contextual event indicators. Lag lengths are chosen based on the descriptive and correlation analysis discussed earlier, reflecting the delayed adjustment typical of housing markets.

Table 1. Baseline model: Kochi Housing Price Index (Assessment)

Dependent variable: HPI (assessment)

Variable	Baseline
Remittances (L1)	0.0005*** (0.0001)
Repo rate (L4)	-5.150*** (1.717)
WPI (All items)	-0.273 (0.434)
Post-flood (step)	0.992 (5.079)
Post-Maradu (step)	-10.918 (7.598)
Post-COVID 2021 (step)	-10.396 (8.672)
Constant	125.228** (46.749)
Observations	46
R²	0.846
Adjusted R²	0.822

Notes: Standard errors in parentheses. * p<0.1; ** p<0.05; *** p<0.01.

Table 1: Baseline regression results

The results indicate a strong and statistically significant positive association between lagged remittance inflows and housing prices. The estimated coefficient suggests that increases in remittance inflows translate into higher housing price levels with a delay of approximately one quarter, consistent with the idea that remittance-financed housing demand operates through gradual investment decisions rather than immediate transactions. This finding remains robust across alternative specifications and underscores the central role of external income flows in shaping Kochi’s housing market.

Monetary policy conditions, proxied by the RBI policy repo rate, enter the model with a negative and statistically significant coefficient at longer lags. This indicates that tighter borrowing conditions are associated with lower housing prices, but only after several quarters, highlighting the slow transmission of monetary policy to real estate markets. The magnitude and lag structure of the repo rate effect are consistent with housing market frictions such as delayed project launches, staggered purchase decisions, and the prevalence of fixed or semi-fixed lending rates.

In contrast, the Wholesale Price Index (WPI) does not exhibit a robust independent effect on housing prices once remittances and monetary conditions are controlled for. While construction costs are often assumed to play a direct role in price formation, the results suggest that demand-side factors dominate in Kochi, with cost pressures absorbed through adjustments in margins and project timing.

The contextual event indicators, including the post-flood and post-demolition variables, do not produce statistically significant level shifts in the baseline specification. This absence of sharp effects supports the interpretation that major shocks influence the housing market through changes in expectations and adjustment dynamics rather than through immediate, discrete re-pricing. Overall, the baseline results point to a remittance-led housing market with delayed and weakening monetary transmission, setting the stage for a closer examination of stability and regime change.

5. Stability, Structural Change, and Robustness

While the baseline model establishes a strong and economically intuitive relationship between Kochi’s housing price dynamics, remittances, and monetary conditions, an important question remains: are these relationships stable across time and major shocks? This section examines parameter stability using split-sample regressions, formal break tests, and rolling-window estimates.

5.1 Pre- and Post-Event Split Regressions

To examine whether the macro–housing relationship changed following major disruptions, the baseline specification was re-estimated across sub-periods defined by two salient structural events: the 2018 Kerala floods and the COVID-19 shock. The results are reported in Table 2.

Table 2. Split regressions and baseline: Kochi HPI (Assessment)

Dependent variable: HPI (assessment)

Variable	Baseline	Pre-flood	Post-flood	Pre-COVID	Post-COVID
Remittances (L1)	0.0005*** (0.0001)	0.001257* (0.000560)	0.000255* (0.000106)	0.000723*** (0.000117)	0.000130 (0.000232)
Repo rate (L4)	-5.150*** (1.717)	-6.857 (4.881)	-1.616 (1.615)	-9.966*** (2.299)	-0.787 (2.726)
WPI (All items)	-0.273 (0.434)	-1.204 (0.689)	-0.155 (0.342)	-1.346** (0.456)	0.121 (0.546)
Post-flood (step)	0.992 (5.079)
Post-Maradu (step)	-10.918 (7.598)
Post-COVID 2021 (step)	-10.396 (8.672)
Constant	125.228** (46.749)	181.617 (137.363)	111.811** (34.861)	261.1*** (59.66)	90.964. (48.800)
Model statistics
Observations	46	17	29	24	22
R²	0.846	0.772	0.618	0.842	0.258
Adjusted R²	0.822	0.720	0.573	0.819	0.134

Notes: Standard errors in parentheses. Significance stars are as reported by your R summaries/baseline output. Split samples are based on your defined flood (2018Q3) and COVID (2020Q2) cutoffs.

Table 2: Baseline and Split Regressions

The split results reveal a clear attenuation of macroeconomic effects over time. In the pre-flood period, remittances retain a strong and statistically significant positive association with housing prices, while the repo rate exhibits a sizeable negative effect. In contrast, in the post-flood period, both coefficients weaken substantially in magnitude and statistical significance. This suggests that the floods marked not merely a short-term disruption, but a shift in the responsiveness of housing prices to macroeconomic drivers.

A similar pattern emerges when the sample is split around the COVID period. In the pre-COVID subsample, all three core macro variables—remittances, interest rates, and construction costs—are statistically significant and economically meaningful. In the post-COVID period, however, none of these relationships remain significant. Housing prices appear far less sensitive to contemporaneous macro signals, consistent with a market shaped by uncertainty, delayed transactions, and non-economic constraints.

Importantly, these results do not imply that macro factors ceased to matter, but rather that their transmission into observed prices became weaker and noisier after successive shocks.

5.2 Formal Structural Break Tests

To formally assess the presence of structural breaks, Chow-style breakpoint tests were conducted on the baseline model at two hypothesised breakpoints: 2018Q3 (flood onset) and 2020Q2 (COVID onset). The test statistics indicate borderline to moderate evidence of parameter instability, particularly around the flood period.

While the null hypothesis of full parameter stability cannot be rejected at conventional 5% levels, the results consistently point toward gradual structural change rather than abrupt regime shifts. This is in line with the institutional and behavioural nature of housing markets, where adjustment tends to occur through slow re-pricing, deferred transactions, and altered expectations rather than immediate discontinuities.

5.3 Rolling Window Regressions and Coefficient Drift

To visualise the evolution of macro effects over time, the baseline model was estimated using rolling eight-quarter windows, tracking the coefficients on remittances, repo rates, and construction costs.

 

The rolling estimates reveal pronounced coefficient drift. The remittance coefficient is large and stable in the mid-2010s, peaks around the pre-flood period, and then steadily declines toward zero in recent years. The interest-rate coefficient exhibits substantial volatility, switching sign in some windows, indicating unstable monetary transmission into housing prices. Construction cost effects likewise weaken and become erratic.

These patterns reinforce the split-sample results: macro drivers explain housing prices well during “normal” periods, but their influence becomes unstable following cumulative shocks.

5.4 Robustness to Trend and Differencing

Finally, robustness checks were conducted by augmenting the baseline model with a deterministic time trend and by estimating the model in first differences. These results are reported in Table 3.

Kochi Housing Price Index – Robustness Models

Model A: Levels with Time Trend

Variable	Coefficient	Std. Error	Significance
Remittances (L1)	0.00034	0.00017	*
Repo Rate (L4)	-4.662	1.732	*
WPI (All)	-0.352	0.433	ns
Post-flood	-1.340	5.286	ns
Post-Maradu	-8.039	7.781	ns
Post-COVID	-7.081	8.886	ns
Trend	0.549	0.391	ns

Observations: 46
R²: 0.854 | Adj. R²: 0.827

Model B: First-Difference Specification

Variable	Coefficient	Std. Error	Significance
ΔRepo	4.532	6.965	ns
ΔWPI	0.728	0.708	ns
Remittances (L1)	0.000085	0.000119	ns

Observations: 46
R²: 0.058

Table 3: Robustness Checks

Including a time trend does not materially alter the main findings: remittances and interest rates remain significant with expected signs, while event dummies and the trend itself are not statistically dominant. In contrast, differenced models exhibit very low explanatory power and insignificant coefficients across variables. This suggests that housing price adjustment in Kochi is slow and level-based, rather than driven by short-term quarterly fluctuations.

5.5 Interpretation

Taken together, these evidence points to a coherent interpretation. Kochi’s housing market exhibits strong macroeconomic anchoring during stable periods, particularly through remittance inflows and borrowing costs. However, repeated shocks like natural disasters, regulatory interventions, and pandemic disruptions have progressively weakened these linkages. Rather than discrete breaks, the market appears to undergo gradual structural erosion in macro responsiveness, consistent with delayed expectations, institutional frictions, and behavioural adjustment.

6. Discussion and Policy Implications

The empirical results point to a housing market in Kochi that is macro-anchored but shock-sensitive. During relatively stable periods, housing prices respond strongly and predictably to remittance inflows and borrowing costs. However, this relationship weakens substantially following cumulative shocks (most notably the 2018 floods and the COVID-19 period) suggesting a transition from a macro-driven market to one shaped increasingly by institutional, behavioural, and risk-related factors.

6.1 Remittances as a Structural Demand Anchor

Across specifications, remittances emerge as the most robust macroeconomic correlate of housing prices, particularly with a one-quarter lag. This aligns closely with Kerala’s long-standing political economy, where housing investment serves both as a consumption good and a store of value for migrant households. The lag structure suggests that remittance income does not translate immediately into transactions but operates through planning, liquidity accumulation, and timing of purchases.

Importantly, the weakening of the remittance coefficient in the post-shock period does not imply declining relevance of remittances per se. Rather, it indicates reduced pass-through into observed prices, possibly due to heightened risk aversion, regulatory uncertainty, and postponed construction or purchase decisions. In this sense, remittances appear to function as a latent stabiliser rather than an immediate price accelerator in the post-shock environment.

6.2 Monetary Transmission and Housing Market Frictions

Interest rates exert a consistently negative effect on housing prices in the baseline and pre-shock models, but this relationship becomes unstable and statistically weak in later periods. Rolling regressions show substantial coefficient volatility, including sign reversals, especially after 2018. This suggests that monetary transmission into housing markets is neither linear nor constant.

One interpretation is that formal lending rates increasingly fail to capture effective financing conditions faced by households. Informal finance, cash purchases, deferred construction, and renegotiated payment structures may dilute the impact of policy rates. In a market like Kochi; characterised by heterogeneous buyers, partial informality, and significant self-financing; repo rate changes alone are insufficient to explain price dynamics during periods of stress.

6.3 Construction Costs and Supply-Side Rigidities

Construction cost proxies (WPI) display limited explanatory power once remittances and interest rates are accounted for. While costs undoubtedly matter for feasibility and margins, their weak statistical role suggests that supply-side adjustment is slow and constrained. Developers may absorb cost shocks through margin compression, project delays, or quality adjustments rather than immediate price pass-through, especially when demand conditions are uncertain.

This reinforces the view that Kochi’s housing market adjusts primarily through quantities and timing, rather than continuous price clearing.

6.4 Shocks, Expectations, and Market Memory

The absence of strong post-event dummy effects, combined with declining explanatory power in post-shock subsamples, points to an important behavioural dimension. Rather than discrete breaks, the evidence supports a process of expectational resetting. Floods, demolitions, and pandemics appear to alter risk perceptions and transaction norms in lasting ways, even after physical reconstruction and policy normalisation.

In this context, housing prices reflect not only contemporaneous macro conditions but also market memory, a cumulative record of past disruptions that dampens responsiveness to otherwise favourable signals.

6.5 Implications for Policy and Analysis

For policymakers, the results caution against relying solely on national monetary instruments to influence regional housing markets. In remittance-dependent, shock-exposed regions like Kochi, housing outcomes are shaped as much by confidence, institutional credibility, and regulatory clarity as by interest rates.

For researchers, the findings underscore the importance of:

• incorporating lag structures rather than contemporaneous macro effects,
• testing parameter stability, not just statistical significance,
• and treating housing markets as path-dependent systems, not frictionless asset markets.

7. Conclusion

This study set out to examine which macroeconomic indicators best track housing price movements in Kochi and whether those relationships remain stable over time. Using a quarterly city-level housing price index combined with national and Kerala-specific macroeconomic series, the analysis finds that Kochi’s housing market is structurally anchored to remittance inflows and borrowing conditions, but only under relatively stable institutional and environmental conditions.

In the baseline models, remittances, particularly with a short lag, emerge as the most consistent correlate of housing prices, reinforcing Kerala’s distinctive migration–housing nexus. Interest rates exert a clear negative influence, indicating that monetary conditions do matter, even in a market characterised by partial informality and self-financing. Construction cost indicators, by contrast, play a secondary role, suggesting that supply-side pressures are absorbed through margins, timing, and project phasing rather than immediate price adjustments.

However, stability tests reveal that these relationships are not invariant over time. The 2018 floods mark a meaningful weakening of macro–price linkages, while the post-COVID period is characterised by sharply reduced explanatory power and heightened coefficient volatility. Rolling regressions show substantial drift in key parameters, indicating that housing prices in Kochi cannot be modelled as the outcome of a single, stable macroeconomic regime. Instead, the market exhibits path dependence, where successive shocks reshape expectations, risk perceptions, and behavioural responses.

An important implication of these findings is that housing prices in Kochi appear to respond more reliably to macro fundamentals during “normal” periods than during phases of recovery or uncertainty. After major shocks, prices become less sensitive to income and interest-rate signals, reflecting postponed decisions, regulatory caution, and altered household priorities. In such contexts, observed prices may understate latent demand, particularly in a remittance-rich economy.

From a policy perspective, the results caution against assuming smooth monetary transmission into regional housing markets. In shock-prone, migration-dependent regions, housing outcomes are shaped as much by confidence, governance, and institutional clarity as by interest rates or cost conditions. For planners and regulators, this highlights the importance of predictable rules, credible enforcement, and post-shock reassurance in restoring market functionality.

Finally, this study demonstrates the value of city-specific, stability-aware housing analysis. Rather than demonstrating whether macro variables “matter” in general, the findings show when and under what conditions they matter. Future research could extend this framework by incorporating micro-level transaction data, spatial heterogeneity within the city, or demographic shifts such as student out-migration. Together, such extensions would further enrich our understanding of housing markets as socially embedded, temporally uneven economic systems.