# RECORD 6edd1ccf2791c69eb6ab0393c5323dfff21ae620 # TITLE: The Impact of the Youth Housing Loan Subsidy Policy on Housing Prices: Evidence from New Taipei City # YEAR: 2026.0 VENUE: Business and Economic Research DOI: 10.5296/ber.v16i4.24024 # AUTHORS: Ching-Hsing Chang; Yi Lin; Wen-Tsung Wu; Chun-Hsien Wang # PAGES: 26 CANDIDATE TABLE PAGES (1-based): [12, 13, 15, 16, 17, 18, 19] # ABSTRACT: In August 2023, the Taiwanese government launched the New Youth Housing Loan Subsidy Policy (hereafter the New Youth Policy), which lowers the threshold for young people to buy homes through interest subsidies, higher loan ceilings, longer maturities, and extended grace periods. However, when housing supply is difficult to increase in the short run, such a demand-side subsidy may push up housing prices through a price capitalization mechanism. Taking New Taipei City as the study area, this paper uses more than one hundred thousand transaction records from the Ministry of the Interior’s actual price registration system between January 2021 and September 2024, and applies a hedonic price model to construct a multiple regression. After controlling for building characteristics, location, and annual trends, the study examines the effect of the policy on the unit price of housing. The pooled specification shows that the unit price of housing was about 4.4% higher in the post-implementation period, a differential equivalent to roughly one-fifth of the conditional appreciation of the market over the same window. To test whether this differential can be attributed to the policy rather than to a market-wide trend, we exploit the NT$10 million loan ceiling to construct a difference-in-differences design in which small dwellings, whose transaction prices fall within the ceiling, are treated as the high-exposure group and large dwellings, whose prices lie far above it, serve as the low-exposure comparison group. Once the control variables are allowed to differ across the two segments, the difference-in-differences estimate is economically and statistically indistinguishable from zero, and this null result is stable across four alternative size thresholds. An event-study specification reveals a significant pre-policy divergence between the two groups, and a placebo cut-off placed in the first half of 2023 yields a significant coefficient, so the parallel-trends assumption is not supported. A split-sample estimation by unit size likewise finds no significant difference in the policy coefficient between small and large dwellings. Building age and floor area have negative effects while floor level has a positive effect, reflecting the phenomenon of higher unit prices for smaller units. We therefore conclude that housing prices in New Taipei City rose markedly over the observation window, but that the data do not provide robust evidence that this increase is attributable to ===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 74 The Impact of the Youth Housing Loan Subsidy Policy on Housing Prices: Evidence from New Taipei City Ching-Hsing Chang Department of Technology Management, National Chiayi University, Chiayi City, Taiwan E-mail: chc@mail.ncyu.edu.tw Yi Lin EMBA Program, College of Management, National Chiayi University, Chiayi City, Taiwan E-mail: irene173956@gmail.com Wen-Tsung Wu (Corresponding author) Chinese Sustainable Innovation Development Association (CSIDA), Taipei, Taiwan E-mail: roywu070679@gmail.com Chun-Hsien Wang Department of Business Administration, National Chiayi University, Chiayi City, Taiwan E-mail: chwang@mail.ncyu.edu.tw Received: July 26, 2026 Accepted: September 9, 2026 Published: September 10, 2026 doi:10.5296/ber.v16i4.24024 URL: https://doi.org/10.5296/ber.v16i4.24024 Abstract In August 2023, the Taiwanese government launched the New Youth Housing Loan Subsidy Policy (hereafter the New Youth Policy), which lowers the threshold for young people to buy homes through interest subsidies, higher loan ceilings, longer maturities, and extended grace periods. However, when housing supply is difficult to increase in the short run, such a demand-side subsidy may push up housing prices through a price capitalization mechanism. Taking New Taipei City as the study area, this paper uses more than one hundred thousand transaction records from the Ministry of the Interior’s actual price registration system ===== PAGE 12 (score 15, numeric-paren 0, table-words 2, var-words 8) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 85 4. Empirical Results and Analysis This chapter analyzes the impact of the New Youth Policy on housing prices in New Taipei City. It first uses descriptive statistics to understand the basic characteristics of the sample; then conducts correlation analysis and a multicollinearity test to ensure the reliability of the model estimates; next applies OLS for regression analysis; then verifies the stability of the results through a robustness check; and finally examines sub-market heterogeneity by housing unit size. The chapter comprises five parts: descriptive statistical analysis; correlation and multicollinearity tests; regression results; the robustness check; and the sub-market heterogeneity analysis. 4.1 Descriptive Statistical Analysis This study collected 108,671 housing transaction samples in New Taipei City. The dependent variable, the log of the housing unit price, has a mean of 3.61 and a median of 3.65; the price distribution is slightly skewed, and log transformation helps reduce skewness and improve model stability. The policy dummy has a mean of 0.31, indicating that about 31% of the samples fall in the post-implementation period, covering transactions before and after the policy. For the control variables, building age averages 22.59 years with a median of 24, indicating that the sample is dominated by second-hand housing; floor area averages 37.28 ping with a median of 33.09, showing a right-skewed distribution; floor level averages 7.26, reflecting a sample that includes both low-rise apartments and high-rise towers; about 45% of the dwellings have parking spaces, 72% have management organizations, and 72% are elevator buildings; in terms of regional distribution, high-price areas account for 58% and mid-price areas for 37%, consistent with the concentration of New Taipei City housing transactions in densely populated and conveniently connected areas; and the annual sample proportions are evenly distributed, effectively reflecting market changes across years, as shown in Table 4. Table 4. Descriptive statistics Variable Mean SD Min Median Max Log of housing unit price 3.61 0.41 0.39 3.65 5.49 New Youth Policy 0.31 0.464 0 0 1 Building age (years) 22.59 14.64 1 24 73 Floor area (ping) 37.28 20.13 0.33 33.09 1172.84 Floor level 7.26 5.67 1 5 43 Has parking 0.45 0.50 0 0 1 Has management 0.72 0.45 0 1 1 Elevator building 0.72 0.45 0 1 1 High-price area 0.58 0.49 0 1 1 Mid-price area 0.37 0.48 0 0 1 2022 0.24 0.43 0 0 1 2023 0.26 0.44 0 0 1 2024 0.20 0.40 0 0 1 Note: N = 108,671; the data period is January 2021 to September 2024. ===== PAGE 13 (score 25, numeric-paren 0, table-words 6, var-words 10) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 86 4.2 Correlation and Multicollinearity Tests This study first uses correlation analysis to examine the degree of association among the variables; the results show that the direction of correlation between each explanatory variable and the log of the housing unit price is broadly consistent with theoretical expectations, and no abnormally high correlation appears among the explanatory variables. A further multicollinearity test using the VIF, shown in Table 5, indicates that the VIF values of the variables range from 1.317 to 5.601, all below the threshold of 10, showing no high collinearity among the explanatory variables and confirming the reliability of the regression estimates. The slightly higher VIF values for elevator buildings, high-price areas, and management organizations reflect a certain degree of association among building type, management mechanism, and location, but remain within an acceptable range. Table 5. Multicollinearity test Variable VIF Tolerance New Youth Policy 3.404 0.294 Building age 3.112 0.321 Floor area 1.562 0.640 Floor level 1.317 0.759 Has parking 2.222 0.450 Has management 4.750 0.211 Elevator building 5.601 0.179 High-price area 5.156 0.194 Mid-price area 5.138 0.195 2022 1.379 0.725 2023 1.979 0.505 2024 3.894 0.257 4.3 Regression Results The regression results are shown in Table 6. The model’s R² and adjusted R² are both 0.533, indicating that the policy, building characteristic, regional, and annual variables included in this study explain about 53.3% of the variation in the log of the housing unit price; the F value is 10346.121, significant at the 1% level, indicating that the overall model is statistically significant. For the main explanatory variable, the coefficient of the New Youth Policy is 0.044 with a t value of 12.829, significant at the 1% level, indicating that after controlling for building age, floor area, floor level, parking, management organization, building type, region, and year, the housing unit price in the post-implementation period is still significantly higher than in the pre-implementation period; since the dependent variable is the log of the unit price, the policy coefficient can be interpreted as an increase of about 4.4% in the housing unit price after implementation. This result suggests that the New Youth Policy may, by lowering the burden of home-purchase loans, raising loan ceilings, and improving financing conditions, bring part of the purchase demand into the market earlier, thereby exerting upward pressure on housing prices in New Taipei City—consistent with the expectation of this study and with the inference of price capitalization theory. ===== PAGE 14 (score 24, numeric-paren 0, table-words 6, var-words 12) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 87 The economic magnitude of this coefficient should be assessed against the contemporaneous appreciation of the market rather than in isolation. The year dummies in Table 6 indicate that, relative to 2021, the conditional unit price of housing in New Taipei City was 9.5% higher in 2022, 14.4% higher in 2023, and 21.3% higher in 2024, implying an average conditional appreciation of roughly 7.1% per year over the observation window. Measured against this benchmark, the 4.4% policy differential is equivalent to approximately one-fifth of the cumulative conditional appreciation recorded between 2021 and 2024, or about seven to eight months of typical market appreciation. Measured against the raw index, the Ministry of the Interior housing price index for New Taipei City rose by about 31% between the first quarter of 2021 and the third quarter of 2024, so the policy differential corresponds to roughly one-seventh of the unconditional price growth of the same period. The interpretation is therefore two-sided. The differential is not trivial: for a dwelling transacting at the sample median it is comparable in order of magnitude to several years of the interest saving that the subsidy itself confers, which is the pattern that price capitalization theory predicts when short-run supply is inelastic. At the same time, the differential is clearly a minority share of the observed price increase, so the New Youth Policy cannot be regarded as the dominant driver of housing price growth in New Taipei City over this period; the great majority of the increase is attributable to the broader market trend captured by the year effects and to locational and structural attributes. Readers should also bear in mind that, for the identification reasons set out in Section 3.4, this 4.4% figure represents an upper bound on the policy contribution to the extent that any unobserved time-varying demand shock coincides with the August 2023 cut-off. The interpretation is therefore two-sided. The differential is not trivial: for a dwelling transacting at the sample median it is comparable in order of magnitude to several years of the interest saving that the subsidy itself confers, which is the pattern that price capitalization theory predicts when short-run supply is inelastic. At the same time, it is clearly a minority share of the observed price increase, so the New Youth Policy cannot be regarded as the dominant driver of housing price growth in New Taipei City over this period. Whether even this minority share can be attributed to the policy is a separate question, and it is the one addressed in Section 4.5, where a difference-in-differences design based on differential exposure to the loan ceiling is used to test whether the differential is concentrated in the segment the policy actually reaches. For the control variables: the coefficient of building age is −0.009 and significant, indicating that each additional year of age reduces the unit price by about 0.9%, reflecting depreciation; the coefficient of floor area is −0.001 and significant, confirming the “small unit, high unit price” phenomenon in the New Taipei City market; the coefficient of floor level is 0.006 and significant, indicating that each additional floor raises the unit price by about 0.6%. The coefficients of parking (−0.080) and management organization (−0.116) differ from the expected direction: the parking variable may be diluted because parking area or ancillary space is included in the per-ping unit price calculation, while management organization is highly correlated with elevator buildings, so its positive effect may have been absorbed by the building type variable; these two results should not be read directly as parking or management reducing housing value, but rather as overlapping explanatory effects and product-structure differences under the per-ping unit price model. The coefficient of elevator ===== PAGE 15 (score 44, numeric-paren 16, table-words 8, var-words 7) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 88 buildings is 0.150 and significant, indicating that the unit price of elevator buildings is about 15% to 16% higher than that of apartments. For the regional variables, the coefficients of high-price areas (0.964) and mid-price areas (0.511) are both significant at the 1% level, indicating that location remains one of the most important factors affecting housing prices. The coefficient of the year variable rises from 0.095 in 2022 to 0.213 in 2024, reflecting the overall continued rise of the New Taipei City housing market during the study period. Table 6. Regression analysis Variable Coefficient t value p value New Youth Policy 0.044 12.829 <0.001*** Building age −0.009 −87.388 <0.001*** Floor area −0.001 −19.028 <0.001*** Floor level 0.006 32.799 <0.001*** Has parking −0.080 −30.929 <0.001*** Has management −0.116 −27.854 <0.001*** Elevator building 0.150 33.041 <0.001*** High-price area 0.964 243.965 <0.001*** Mid-price area 0.511 126.682 <0.001*** 2022 0.095 40.181 <0.001*** 2023 0.144 52.217 <0.001*** 2024 0.213 50.526 <0.001*** Intercept 2.959 477.348 <0.001*** F value 10346.121 <0.001*** R² 0.533 Adjusted R² 0.533 Note 1: N = 108,671; the dependent variable is the log of the housing unit price. Note 2: *** significant at 1%, ** at 5%, * at 10%. 4.4 Robustness Check To confirm the reliability of the baseline estimates, this study, based on the standardized residuals of the baseline model, removes observations whose absolute standardized residual exceeds 3, so as to reduce the interference of abnormal transaction prices; after this treatment, the valid sample decreases from 108,671 to 107,518, removing 1,153 extreme observations, about 1.06% of the total. As shown in Table 7, the model after removing extreme values (Model 2) is highly consistent with the baseline model (Model 1) in the direction and statistical significance of the coefficients: the coefficient of the core explanatory variable, the New Youth Policy, is slightly adjusted from 0.044 to 0.043, remaining positive and significant at the 1% level; the coefficients and significance of the building characteristic variables are almost identical; and although the coefficients of high-price and mid-price areas decline slightly, their direction and significance are unchanged. The R² of Model 2 rises from 0.533 to 0.557 and its F value to 11273.002, indicating an improvement in the overall explanatory power after removing extreme values. In sum, the significant positive impact of the New Youth Policy on housing prices in New Taipei City does not change with the minor adjustment of the sample composition, and the empirical findings of this study exhibit good robustness. ===== PAGE 16 (score 53, numeric-paren 26, table-words 7, var-words 9) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 89 Table 7. Estimation results of the baseline model and the robustness check model Variable Baseline (Model 1) After removing outliers (Model 2) New Youth Policy 0.044*** 0.043*** Building age −0.009*** −0.009*** Floor area −0.001*** −0.001*** Floor level 0.006*** 0.006*** Has parking −0.080*** −0.078*** Has management −0.116*** −0.115*** Elevator building 0.150*** 0.149*** High-price area 0.964*** 0.897*** Mid-price area 0.511*** 0.451*** 2022 0.095*** 0.097*** 2023 0.144*** 0.149*** 2024 0.213*** 0.217*** Intercept 2.959*** 3.018*** F value 10346.121 11273.002 R² 0.533 0.557 Adjusted R² 0.533 0.557 N 108,671 107,518 Note 1: The dependent variable is the log of the housing unit price. Note 2: *** significant at 1%, ** at 5%, * at 10%. 4.5 Sub-market Heterogeneity and a Difference-in-Differences Assessment The New Youth Policy is not directed at the housing market as a whole. Eligibility is restricted to first-time owner-occupier buyers with no other self-use residence, and the loan is capped at NT$10 million, so the scheme reaches dwellings with a low total-price threshold far more than it reaches large residences. If the 4.4% differential estimated in Section 4.3 reflects capitalization of the subsidy rather than a market-wide trend, it should be concentrated in the segment the policy actually touches. This section tests that proposition in two ways: first by re-estimating the baseline model on size-split sub-samples, and then by using the exposure contrast described in Section 3.4 to construct a difference-in-differences estimator with an event study and placebo tests. 4.5.1 Split-sample Estimation by Housing Unit Size Table 8 reports the baseline specification estimated separately for dwellings below and at or above 25 ping, with heteroskedasticity-robust standard errors. The policy coefficient is 0.040 in the small-unit sub-sample and 0.045 in the large-unit sub-sample. Both are significant at the 1% level, but they are almost identical in magnitude, and the difference between them is −0.005 with a standard error of 0.008 (z = −0.67, p = 0.503). The difference is therefore statistically indistinguishable from zero, and its sign runs opposite to the direction that price capitalization would predict. The remaining coefficients differ substantially across the two sub-samples — the high-price-area coefficient is 1.241 among small units against 0.845 among large units, and the floor-area coefficient is an order of magnitude larger in absolute value among small units — which indicates that the two segments are governed by materially different price structures. This observation matters for the specification choices reported below. ===== PAGE 17 (score 82, numeric-paren 53, table-words 7, var-words 9) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 90 Table 8. Split-sample estimation by housing unit size Variable Small units (< 25 ping) Large units (≥ 25 ping) New Youth Policy 0.040*** (0.007) 0.045*** (0.004) Building age −0.008*** (0.000) −0.010*** (0.000) Floor area −0.003*** (0.000) −0.000*** (0.000) Floor level 0.004*** (0.000) 0.005*** (0.000) Has parking −0.015* (0.009) −0.076*** (0.003) Has management −0.094*** (0.011) −0.114*** (0.005) Elevator building 0.130*** (0.013) 0.149*** (0.006) High-price area 1.241*** (0.013) 0.845*** (0.007) Mid-price area 0.788*** (0.013) 0.397*** (0.007) 2022 0.092*** (0.005) 0.095*** (0.003) 2023 0.145*** (0.006) 0.144*** (0.003) 2024 0.227*** (0.009) 0.209*** (0.005) Intercept 2.743*** (0.019) 3.033*** (0.009) R² 0.591 0.526 Adjusted R² 0.590 0.526 N 27,708 80,963 Difference in policy coefficient −0.005 (0.008), z = −0.67, p = 0.503 Note 1: The dependent variable is the log of the housing unit price. Note 2: Heteroskedasticity-robust (HC1) standard errors in parentheses. Note 3: *** significant at 1%, ** at 5%, * at 10%. Note 4: The difference in the policy coefficient is tested as an unpaired comparison of the two sub-sample estimates. 4.5.2 Difference-in-differences Based on Differential Exposure Following the design set out in Section 3.4, dwellings of 35 ping or less are treated as the high-exposure group (N = 59,428) and dwellings larger than 50 ping as the low-exposure comparison group (N = 20,341), with the intermediate band excluded. The estimating equation adds a treatment indicator, period fixed effects for the five sub-periods, and the interaction of the treatment indicator with the post-policy indicator, which is the coefficient of interest. Table 9 reports the results. Specification (1) constrains the coefficients on the control variables to be equal across the two exposure groups and yields an estimate of 0.015, significant at the 1% level. Specification (2) relaxes that constraint by interacting the treatment indicator with the full set of controls. The estimate falls to −0.001 with a p value of 0.825. Given the evidence in Table 8 that the price structures of the two segments differ sharply, specification (2) is the appropriate one, and specification (1) is misspecified: it imposes a common linear floor-area effect across a range running from below 35 ping to above 50 ping, and a common locational premium, neither of which the data support. The null result in specification (2) is not an artefact of the chosen threshold. Rows (5) to (7) re-estimate the same specification with three alternative definitions of the exposure groups, and the estimate remains between −0.001 and 0.000 with p values above 0.83 throughout. Rows (3) and (4) show, in addition, that even the restricted estimate of 0.015 ceases to be statistically significant once standard errors are clustered to allow for serial correlation within group-period cells, which is the concern raised by Bertrand et al. (2004). We note that our setting is the canonical two-group, multiple-period case rather than a staggered-adoption design, so the aggregation problems documented for two-way fixed-effects estimators under heterogeneous timing do not arise here; the binding ===== PAGE 18 (score 29, numeric-paren 8, table-words 9, var-words 1) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 91 constraint is instead the credibility of the parallel-trends assumption itself (Baker et al., 2026). Table 9. Difference-in-differences estimates of the policy effect Specification DID estimate p value (1) Control coefficients restricted to be equal across groups 0.015*** (0.005) 0.002 (2) Control coefficients allowed to differ across groups −0.001 (0.005) 0.825 (3) Specification (1), SE clustered by group × period (10) 0.015 (0.015) 0.296 (4) Specification (1), SE clustered by group × period × tier (30) 0.015 (0.017) 0.359 (5) Threshold ≤ 30 / > 55 ping, specification (2) −0.001 (0.006) 0.889 (6) Threshold ≤ 40 / > 50 ping, specification (2) −0.001 (0.005) 0.832 (7) Threshold ≤ 35 / > 45 ping, specification (2) −0.000 (0.005) 0.991 Note 1: The dependent variable is the log of the housing unit price. The reported coefficient is the interaction of the high-exposure indicator with the post-policy indicator. Note 2: All specifications include period fixed effects for the five sub-periods and the full set of control variables. Note 3: HC1 robust standard errors in parentheses unless clustering is stated. Note 4: *** significant at 1%, ** at 5%, * at 10%. 4.5.3 Event Study and Placebo Tests Table 10 reports an event-study specification in which the treatment indicator is interacted with each period, taking 2021 as the base, together with two placebo tests estimated on pre-policy data alone. Under the unrestricted specification the interaction for the first half of 2023 — a period that precedes the policy — is −0.029 and significant at the 1% level, which is a direct violation of the parallel-trends assumption: the two exposure groups were already diverging before the policy took effect. The interaction for the first post-policy period, the last five months of 2023, is −0.038 and also significant, meaning that the relative price of the high-exposure segment fell rather than rose in the months immediately following implementation. The coefficient for 2024 is 0.007 and not significant. Under the restricted specification the pattern is similarly non-monotonic, with a significantly negative coefficient in the first post-policy period followed by a positive coefficient a full year later. Neither timing pattern is consistent with the immediate capitalization of a subsidy into the prices of the dwellings that the subsidy finances. The placebo tests point in the same direction. Assigning a false cut-off at the start of 2022 produces a coefficient of 0.002 that is not significant, which is reassuring. Assigning a false cut-off at the start of 2023, however, produces a coefficient of −0.016 that is significant at the 5% level, even though no policy change occurred at that date. Taken with the event-study evidence, this indicates that differential movements between the two exposure groups arise in the pre-policy period as well, and that the identifying assumption required for a causal reading of the difference-in-differences estimate does not hold in these data. ===== PAGE 19 (score 31, numeric-paren 15, table-words 3, var-words 8) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 92 Table 10. Event study and placebo tests Period (base = 2021) Restricted specification Unrestricted specification 2022 (pre-policy) 0.006 (0.006) −0.009 (0.006) 2023 Jan-Jul (pre-policy) −0.006 (0.007) −0.029*** (0.007) 2023 Aug-Dec (post-policy) −0.017** (0.008) −0.038*** (0.008) 2024 (post-policy) 0.035*** (0.006) 0.007 (0.006) Placebo: cut-off set at 2022 0.002 (0.005) — Placebo: cut-off set at 2023 Jan −0.016** (0.007) — Note 1: The dependent variable is the log of the housing unit price. Entries are the interaction of the high-exposure indicator with the stated period. Note 2: The restricted specification constrains control coefficients to be equal across groups; the unrestricted specification allows them to differ. Note 3: Placebo tests are estimated on pre-policy data only (2021 to July 2023). Note 4: HC1 robust standard errors in parentheses. *** significant at 1%, ** at 5%, * at 10%. 4.5.4 Interpretation Three findings emerge, and they point the same way. The split-sample estimation finds no significant difference in the policy coefficient between the segment the policy targets and the segment it does not. The difference-in-differences estimate, once the specification is allowed to accommodate the different price structures of the two segments, is indistinguishable from zero across every threshold we examined. And the identifying assumption that would license a causal reading of that estimate is rejected by both the event study and one of the two placebo tests. We therefore do not find robust evidence that the 4.4% differential reported in Section 4.3 is attributable to price capitalization of the New Youth Policy. This conclusion requires one important qualification, which follows from the third assumption discussed in Section 3.4. A difference-in-differences estimator recovers a differential effect, not a level effect. If the chain move-up mechanism documented by Acolin et al. (2016) operates in the New Taipei City market, price pressure originating among first-time buyers of small dwellings would be transmitted to the larger dwellings that constitute our comparison group, and a uniform market-wide policy effect would be differenced away. Our results therefore establish that the policy did not raise prices in its target segment relative to the non-target segment; they do not establish that the policy had no effect on the market as a whole. What they do remove is the principal empirical support for the capitalization interpretation as it is usually stated, namely that the subsidy is bid into the prices of exactly those dwellings that eligible buyers compete for. The more parsimonious reading of the evidence is that the post-August-2023 price differential mainly reflects the broader upswing of the New Taipei City market during the observation window, which the year dummies capture only imperfectly. 5. Conclusions and Recommendations 5.1 Conclusions This study investigates the impact of the New Youth Policy on housing prices in New Taipei City, using transaction-level actual price registration data as the sample, building a multiple regression model through OLS with the log of the housing unit price as the dependent variable, and incorporating building age, floor area, floor level, building type, parking, ===== PAGE 20 (score 12, numeric-paren 0, table-words 0, var-words 12) ===== Business and Economic Research ISSN 2162-4860 2026, Vol. 16, No. 4 http://ber.macrothink.org 93 management organization, district, and year as control variables to reduce the interference of housing heterogeneity. According to the pooled empirical results, the New Youth Policy variable is significantly and positively associated with housing prices, indicating that after controlling for building characteristics, locational conditions, and year effects, housing prices after implementation remain significantly higher than before. This suggests that the policy may stimulate housing demand reflected in rising prices by lowering the purchase threshold, raising loan ceilings, and easing the financial pressure of home purchase. The further tests reported in Section 4.5, however, do not sustain that reading. Splitting the sample by housing unit size yields policy coefficients of 0.040 and 0.045 for small and large dwellings respectively, a difference that is statistically indistinguishable from zero. A difference-in-differences estimator that exploits the NT$10 million loan ceiling to separate high-exposure from low-exposure dwellings returns an estimate of −0.001 once the control coefficients are allowed to differ between the two segments, and this null is stable across four alternative thresholds. An event study shows a significant divergence between the two groups in the first half of 2023, before the policy took effect, and a placebo cut-off placed at the start of 2023 is significant at the 5% level, so the parallel-trends assumption underpinning the design is not supported. Taken together, these results indicate that housing prices in New Taipei City rose markedly over the observation window, but that the data do not provide robust evidence that the increase is attributable to price capitalization of the New Youth Policy in the segment the policy targets. The 4.4% pooled differential should therefore be read as a conditional association that most probably overstates the policy contribution, and the more parsimonious explanation is that it largely reflects the broader market upswing that the year dummies capture only imperfectly. We note one qualification: because a difference-in-differences estimator recovers a differential rather than a level effect, and because the chain move-up mechanism may transmit price pressure from the target segment to the comparison segment, our results do not rule out a uniform market-wide policy effect. What they remove is the principal empirical support for the capitalization interpretation as it is conventionally stated. This finding is consistent with the view in the literature that the price response to a demand-side subsidy is conditional on local supply elasticity and on the design of the scheme rather than automatic, and it shows that when housing supply is difficult to increase quickly in the short run, a purchase subsidy policy may be partly converted into a housing price increase. For building characteristics, building age has a significant negative effect on housing prices, reflecting depreciation and market preference; floor level has a significant positive effect, indicating that higher floors are more favored owing to better lighting, ventilation, and views; and floor area has a significant negative relationship, confirming the “small unit, high unit price” phenomenon, as smaller products with lower total-price thresholds are favored by first-time buyers and investment demand. In terms of product characteristics, the prices of elevator buildings are significantly higher than those of apartments, indicating that product type has a clear influence on price; although the parking and management variables are significant, their direction differs from expectation, reflecting the effects of product structure, pricing method, and overlapping explanatory power under the per-ping unit price model, and