Assingment: Hedonic Pricing

VRIJE UNIVERSITEIT AMSTERDAM, URBAN ECONOMICS CHALLENGE AND POLICIES

Hedonic Pricing Model Assignment Nefertari PRAMUDHITA(2699855), Dirga Imam Gozali SUMANTRI (2731982) GROUP 10 1. Introduction After 50 years since it was first initiated and 16 years of construction, Amsterdam Noord-Zuid (M52) metro line that runs across the IJ river is finally running in 2018. The establishment of the M52 line makes travel time from Noordepark to Zuid station only 14 minutes. In previous years, the Noord area across the river was not considered an attractive stay location. While the Zuid area is the central business district where the location of big companies is centralized, the M52 line creates a new exciting choice to consider the Noord area a place to live because it is more accessible to the business centre of Amsterdam.

Some judgments assume the M52 metro line increases housing prices near the station. Some empirical kinds of literature may support this opinion. Several types of research have investigated the externalities and their impact on housing prices. Ossokina and Verweij (2015) found that decreasing the traffic density by 50% will increase the housing price by 1.4%. Gupta et al. (2022) point out the 8% increase in real estate prices, which benefit from transit spillover.

In this assignment, we use hedonic analysis of housing transaction data nearby Amsterdam’s metro network to estimate the effects of proximity to the metro station on the housing sales price. A hedonic price function describes the equilibrium prices of varieties of a heterogeneous good, influenced by supply and demand. This method is used in the real estate market to determine the price of a building or land by the property's characteristics and its surrounding environment. The available housing price data is applied with other internal attributes and their surrounding characteristics to estimate the value of hedonic house pricing.

The research question of this assignment is: How is the impact of the metro station accessibility on the housing price. We inform public policymakers to give recommendations using different functional forms. We estimate the total benefits of Amsterdam’s metro network and observe the potential benefit that may or may not be capitalized on the price.

We proceed as follows. In Section 2, we discuss the strategy for metro impact on housing value.

Section 3 discusses the identification result and Section 4 concludes the main findings and potential limitations are

2. Methodology This section will explain how the hedonic price can explain the impact of the metro station accessibility on the housing price. In this discussion we will first implore the linear model on the pricing. Written as an equation, we can describe the model as below

As a comparison, we estimate the same set of variables using log-log specification by taking the log of housing prices and the distmetro and all non dummy variables. The equation of the model can be seen below.

We implement some changes to the control variable to reduce bias because of the probable omitted variable. We first implement a basic model with 4 control variables. We then extend the model to have 5 more variables that are deemed necessary in the data that we have. We will next refer to this model as the extended model. The variable selection of each model will be discussed in the later section of this paper.

To explore the relationship of price and distmetro even further, we also estimating flexible-form models using only housing prices and the distance to new metro stations variable without imposing any specific structure on the functional form

With non-parametric models we can see the relationship between price and distmetro without previously determining the structure of the relationship between the two variables. This provides flexibility for explanations that are closer to reality as linear models do not describe well. We are using local linear regression with bandwidth of .8. However this model can also suffer from omitted variable bias that can make the estimation inconsistent. To tackle this issue, we include the same control variable to the model we use in the linear extended model we discussed before.

Furthermore, to see the value of the benefits of this metro construction project, we estimate the benefits of each model that we have. We estimate by considering the average price of houses around the metro station with houses that are considered not to be affected by the metro station.

This is then used as a basis for comparison with the cost required to build the new metro line, which is 3.1 billion euros.

3. Result 1.A) We have generated two new attributes avspace_room and avprice_sqm on the data. The table of summary statistics and figure of histograms for price, floorspace, n_rooms, distmetro, avspace_room and avprice_sq are presented below.

Referring to the histogram result, the distmetro variable has outlier data. Outlier data was the max value of the distmetro that can be seen skewing the histogram of the distmetro quite dramatically. We implement distmetro > 50000 to exclude the outlier. We exclude the outlier data and generate a new table.

1.B) The motivation we choose some extended variables are: basement, view, and garden are expected to give a positive impact on house price, busyroad is expected to give negative effects. And for the disttram is considered as a relevant predetermined variable where both metro and tram are public transports. These variables are statistically significant at the 1% level.

1.C) The logarithmic version of the hedonic price function is depicted in Figure.2, where column 3 refers to the basic model, and column 4 refers to the extended model. On the basic model, the housing price increases 0.075%, where the 1% distance per unit is closer to the metro station.

The 0.063% distance per unit is more relative to the metro station compared to the extended model. It indicates there might be other factors that influence the housing price. To observe the overall performance, the r-squared between the basic model is 0​​.709, and the extended model is 0.719. The extended model gives a better fit for the model.

With the extended control, we found that the change of 1 meter of distance to the metro station will decrease the house price by 13.46 euro in the linear model. In the log-log model, we found that at the average value, the increase of 1% of distance to transit will decrease house price 0.06%. This finding is much lower than Gupta et al. (2022) finding of 8%.

1.D) For the linear model, the marginal effect of the model we can take from the first derivative of the equation. That said, the equation for marginal effect is β . It is quite clear that the first derivative of the linear equation would yield β. In other words, the marginal change of price over distance is constant; β. To make things even more clear, we plot the marginal effect of distmetro (x-axis) over price (y-axis) as seen figure 3 (left):

And for the log-log equation, we derive the change of price and distmetro and find the marginal effect is β x (price/distmetro). The last equation tells us that as long as the ratio of price stays the same, the expected outcome variable would also stay the same. In other words, the β percent change of distmetro would translate to a percent change of the price. We also plot the marginal effect of distmetro (x-axis) over price (y-axis) for the log-log model, as seen in figure 3 (right).

1.E) In selecting a metro station location, the planner may consider the characteristics of the surrounding location. For example, the metro stations are built to increase accessibility in areas with low housing prices with the intention to increase prices in those areas. If so, the estimate we did previously suffers from reverse causality in which housing price also affects the distance to the new metro. This causes the estimate to be biased.

We can overcome this by providing more control variables, but we cannot ensure that our variables sufficiently eliminate this effect. In the next section we will discuss semi-parametric and non-parametric regression, which gives the model flexibility to explain price at a distance to the metro without imposing the strict structure on the data.

2.A) The previous discussion revolved around the parametric model of which we impose the particular structure for our estimation to the data. However, this approach might lead to the loss of a particular characteristic of marginal effect due to the predetermined structure we impose.

From the graph above we can see it is based on a non-parametric model using local linear regression with the bandwidth of .8. We can see that the slope of the graph differs at about 1000m for the distmetro. The value is still consistent with our linear model in the sense that there is a downward slope to the relation of price and distmetro.

2.B) As has been discussed in section 2, the absence of any control variable will probably bias our estimation result. Therefore, we implement the control variable as a semi-parametric regression. We use the same control variable as we use in the extended model.

From the graph above, we can see it is based on a semi-parametric model using local linear regression with a bandwidth of .8 with the same control variable as the extended model. We can see that there is an upward slope price to the distmetro until the value of 1000m. This value is rather intriguing because it is straying from our expectation that the closer housing is to the metro station, the more expensive it becomes. This is also showing the non-linear relationship the price and distmetro has that we otherwise would not capture with imposing the linear model on the previous discussion.

2.C) To have a better comparison between the models, we predicted the value of each model in some intervals. We take 10m as the margin of error. Just under 100m, for example, means that it will contain all observations that are in between 90m to 100m distance from metro station. We do this for 100m, 200m, 400m, 600m, 1000m, and 2000m to see the price changes to the distance to the metro station from some of the models we did before.

It can be seen that as predicted, the closer to the metro, the higher the value of the surrounding houses. As a place-based policy, the metro seems to be successful in increasing prices in depressed areas. However, evaluation based on a semi-parametric specification will suggest that housing price is a concave function with respect to the distance to new stations. As discussed in the previous section, at the range of 0-1 km, the price of housing is decreasing with the reduction in the distance to metro stations.

The possible explanation for this finding is that people value the accessibility but not the crowding that metro might bring. Hence, to live near the metro station but not so close is more desirable than to live right next to the station.

2.D) To compare the costs and benefits of this project, we calculated the projected benefits of increasing house prices due to the metro station. For simplicity, assume that there are 400,000 households living within a 2 km radius of the metro station and that the population is distributed evenly over space. We estimate the average premium on houses value located within 2 km of a metro station. As for houses beyond this range we assume the effect of metro stations is negligible; hence we put it as the base line.

Specification

Average house value beyond 2km (base line)

Average house value within 2km

Number of houses within 2km

Average increase in value

Aggregate increase in value (projected benefits)

(1)

(2)

(3)

(4)=(1)-(2)

(3)*(4)

linear regression

258,306.8

293,833.2

400,000

35,526.4

14,210,560,000

log-log regression

259,652.8

288,600.3

400,000

28,947.5

11,579,000,000

non-parametric

259,395.6

292,983.7

400,000

33,588.1

13,435,240,000

semi-parametric

258,777.3

291,685.5

400,000

32,908.2

13,163,280,000

Table.4 Projected Benefits Estimation We compare all the forms that we estimated. We found that the increase of accessibility caused by the new metro line has a positive impact. From our estimation showing that the projected benefit of this Metro build is 11.57 billion euro at the smallest from log-log model estimation, and 14.21 billion euro at largest from linear model estimation. Both non-parametrik and semi-parametric models have pretty similar benefits, 13.43 billion euro and 13.16 billion euro respectively. Considering the Noord-Zuid line construction needed an estimated capital cost of 3.1 billion euro, with our estimation, the project of building a new metro line has an overwhelming benefit that definitely outweighs the cost.

4. Conclusion Metro Line 52 was built to improve Noord accessibility in city central. This is reported to have a positive relationship with increasing house prices in areas that were previously considered underdeveloped areas. This study shows that with linear, log-log, and non-parametric models, there is a relationship between distance to the metro station and house prices. This finding is in line with Gupta et al. (2022); however, we find the relation on a much lower scale. The analysis using a semi-parametric model by controlling the characteristics of the house as done in the linear and log-log model shows that the price of 0-1 km houses increases in price along with the increase in distance to the metro station.

The plausible reasoning for this relationship is that people appreciate the accessibility that is offered by the metro station but despise the crowding that comes with it; hence they will prefer to live a little bit further from the station but still at a reasonably close distance. The relationship for more than 1 km remains the same relationship with other models; decreases in price with increasing distance. However, the benefit is four times larger than the investment, and it is only from the housing market. We also found that the estimated benefit that comes from this project overwhelmingly outweighs the cost.

The secondary effect has not been captured in this research. The impact of housing prices will create richer people that can afford the house. The wealthier people will spend more and increase the number of amenities in surrounding areas and might increase other goods prices.

These positive externalities cannot be capitalized into local houses. The project might also have the effect of air pollution plausible externalities that we do not assess.

However, this assessed benefit also raises the question of fairness. As with all place-based policies, there might still be a sorting problem. It might still act as an exogenous amenity that will affect housing choices. Especially when we consider the type of housing that is mostly rented in Amsterdam, then the housing mobility is higher than if the housing is mostly owned. This may make the people who realize the benefit are, not the group that was previously thought. Further research about gentrification might be needed.

5. Bibliography Gupta, A., Van Nieuwerburgh, S., & Kontokosta, C. (2022). Take the Q train: Value capture of public infrastructure projects. Journal of Urban Economics, 129, 103422. https://doi.org/10.1016/j.jue.2021.103422 Ossokina, I. V., & Verweij, G. (2015). Urban traffic externalities: Quasi-experimental evidence from housing prices. Regional Science and Urban Economics, 55, 1–13. https://doi.org/10.1016/j.regsciurbeco.2015.08.002 Verstraete, G. (2013). 6. Underground Visions: Strategies of Resistance along the Amsterdam Metro Lines. In C. Lindner & A. Hussey (Eds.), Paris-Amsterdam Underground (pp.

77–96). Amsterdam University Press. https://doi.org/10.1515/9789048518203-008

Appendix Stata command clear all set more off *read data cd "D:\USER\Documents\My Files\KULIAH\STREEM VU\Urban Problem\Project" use"UECP_T1.dta" *1A *make new variable gen avspace_room = (floorspace/n_room) gen avprice_sqm = (price/floorspace) *summarize variable sum price floorspace n_rooms distmetro avspace_room avprice_sqm *create histogram histogram price histogram floorspace histogram n_rooms histogram distmetro //here we find an outlier skewing the data quite dramatically histogram avspace_room histogram avprice_sqm *drop the outlier drop if distmetro>50000 histogram distmetro // to check the distribution again *1B *change the label for year tab year, gen(year) label variable year1 "2005" label variable year2 "2006" label variable year3 "2007" label variable year4 "2008" label variable year5 "2009" label variable year6 "2010" label variable year7 "2011" label variable year8 "2012" label variable year9 "2013" label variable year10 "2014" label variable year11 "2015" *regression for basic model xi: quietly regress price distmetro floorspace n_rooms i.type_prop_sub i.construction_period year2-year11,r estadd local hasrep "No" est store regbasic *extended model

xi: quietly regress price distmetro floorspace n_rooms i.type_prop_sub i.construction_period year2-year11 busyroad basement view garden disttram,r estadd local hasrep "Yes" est store regext gen MC_reg =_b[distmetro] //generate beta for visualization predict pred_reg, //generate prediction label variable pred_reg "linear regression" *1C *transforming variable gen lnprice = log(price) gen lndistmetro = log(distmetro) gen lndisttram = log(disttram) *log-log regression *basic control xi: quietly regress lnprice lndistmetro floorspace n_rooms i.type_prop_sub i.construction_period year2-year11 estadd local hasrep "No" est store reglnbasic *extended xi: quietly regress lnprice lndistmetro floorspace n_rooms i.type_prop_sub i.construction_period year2-year11 busyroad basement view garden lndisttram estadd local hasrep "Yes" est store reglnext predict pred_logreg //generate prediction gen pred_antilog=exp(pred_logreg) gen MC_logreg =_b[lndistmetro]*(price/distmetro) //generate beta for visualization label variable pred_antilog "log-log regression" *generate report esttab regbasic regext reglnbasic reglnext, se ar2 drop(_Itype_prop* _Iconstruct* year* floorspace n_rooms busyroad basement view garden disttram lndisttram) scalars("hasrep Ext. control") *1D *marginal effect of distmetro (x-axis) on price (y-axis) sort distmetro twoway line MC_reg distmetro twoway line MC_logreg distmetro *2A sort distmetro lowess price distmetro, gen(yhat) twoway line yhat distmetro

label variable yhat "non-parametric" *2B plreg price _Itype_prop* _Iconstruct* year* floorspace n_rooms busyroad basement view garden disttram, nlf(distmetro) gen(yhat2) twoway line yhat2 distmetro label variable yhat2 "semi-parametric" *2C *make group for each distance gen distgroup = cond(inrange(distmetro,90,100),100,cond(inrange(distmetro,190,200),200 ,cond(inrange(distmetro,390,400),400,cond(inrange(distmetro,590,600),6 00, cond(inrange(distmetro,990,1000),1000,cond(inrange(distmetro,1990,2000 ),2000,0)))))) *make mean table sort distgroup by distgroup: eststo: quietly estpost summarize pred_reg pred_antilog yhat yhat2 esttab est2 est3 est4 est5 est6 est7, cells("mean")label nodepvar *2D *find average of below 2000m price gen dist2000 = cond(inrange(distmetro,0,2000),"within 2km","beyond 2km") sort dist2000 by dist2000 :eststo: quietly estpost summarize pred_reg pred_antilog yhat yhat2 esttab est8 est9, cells("mean")label nodepvar *eststo clear