metadata: authors: 0: author: Iimi,Atsushi
count: Madagascar
docna: 0: docna: Location, Location, Location Revisited :
            Evidence from Antananarivo, Madagascar
docty: Policy Research Working Paper
subtopic: Transport Services,Urban Housing and Land Settlements,Urban Housing,Urban Governance and Management,Municipal Management and Reform,Natural Disasters
teratopic: Transport,Communities and Human Settlements,Urban Development,Public Sector Development,Environment
abstracts: cdata!: Understanding how land prices are
            determined is of particular importance for policy makers;
            however, there is little evidence in African countries,
            which are currently experiencing rapid urbanization. The
            paper examines the relationship between land prices and
            locational characteristics using data from Antananarivo, the
            capital of Madagascar. It is found that the land value
            gradients are relatively steep, indicating that the land and
            housing prices tend to overshoot in the middle of the city,
            pushing the poor away from the city to suburban areas. It is
            also found that access to transport infrastructure and
            services, such as minibuses, is an important determinant of
            land value. Not only transport connectivity, but also other
            factors, such as proximity to amenities and administrative
            centers, are found to be important. Better land management
            and urban transport policies are called for to promote these
            aspects in the city.
geo_reg_key: 517191
repnme: repnme: Location, Location, Location Revisited :
            Evidence from Antananarivo, Madagascar
display_title: Location, Location, Location
            Revisited : Evidence from Antananarivo, Madagascar
listing_relative_url: /research/2019/08/31315495/location-location-location-revisited-evidence-antananarivo-madagascar
topicv3: Transport m1326661 135,Transport Infrastructure m1327632 1803,Land and Housing m1327648 817,Population, Aging and Health m1327736 640,Flood m1503315 3216
publishtoextweb_dt: 2019-08-06T00:00:00Z
historic_topic: Transport,Communities and Human Settlements,Urban Development,Public Sector Development,Environment
no_of_pages: 22
author_keys: 0: author_key: 914707
model_extractions: Policy Research Working Paper 8958 Location, Location, Location Revisited Evidence from Antananarivo, Madagascar _Atsushi Iimi_ Transport and Digital Development Global Practice August 2019
Policy Research Working Paper 8958

# **Abstract**

Understanding how land prices are determined is of particular importance for policy makers; however, there is little evidence in African countries, which are currently experiencing rapid urbanization. The paper examines the relationship between land prices and locational characteristics using data from Antananarivo, the capital of Madagascar. It is found that the land value gradients are relatively steep, indicating that the land and housing prices tend to overshoot in the middle of the city, pushing the poor away from the city to suburban areas. It is also found that access to transport infrastructure and services, such as minibuses, is an important determinant of land value. Not only transport connectivity, but also other factors, such as proximity to amenities and administrative centers, are found to be important. Better land management and urban transport policies are called for to promote these aspects in the city.

This paper is a product of the Transport and Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Location, Location, Location Revisited: Evidence from Antananarivo, Madagascar**

## Atsushi Iimi<sup>¶</sup>

Africa Infrastructure, Transport Global Practice World Bank Group Key words: Land price gradient; Spatial autoregressive model. JEL classification: C21, C26, O18, R41

> ¶ Corresponding author.
- 2 -

# **I. INTRODUCTION**

1. How the land values are determined has long been the central topic of urban economics. As summarized by Atack and Margo (1998), in theory, the land value declines with distance from the central business district (CBD) in a simple monocentric city model. The land value gradient also flattens as a city expands and transport connectivity improves. With various locational characteristics developed, the power of this monocentric urban model may decrease. Atack and Margo show that the land prices in New York City evolved in this way during the nineteenth and twentieth centuries.

2. More recent studies discuss multidimensionality and complexity of land value determinants. “Land” constitutes not only land itself but also all aspects of a developed property, including location, property attributes and neighborhood amenities (Davis, 2009; Jaeger, 2013; Chen et al., 2017). With data between 2003 and 2011, Gedal and Ellen (2018) show that the land value gradient in New York is relevant to the distance to CBD but also affected by neighborhood attributes, such as proximity to quality parks and neighborhood demographics, including local poverty and ethnicity. In China, land prices vary across categories of land use: While residential land prices depend more on accessibility to the CBD, commercial land prices are more sensitive to proximity to rail stations (Qin et al., 2016). In the Singapore condominium market, the proximity to metro stations and good schools (i.e., top 10 secondary schools and universities) is important (Tu et al., 2007).

3. Currently, Africa is experiencing very rapid urbanization. According to UN-Habitat (2016), for example, the population of Dar es Salaam, which is the largest city in East and Southern Africa, increased from 1.8 million in 1995 to 5.1 million in 2015 ( **Figure 1** ). It is followed by Nairobi (3.9 million), Addis Ababa (3.2 million) and Antananarivo (2.6 million). These cities have been growing at about 5 to 6 percent per annum – nearly twice as high as the total population growth – and are expected to continue growing. In Africa, however, land use and urban transport policies often lag behind such rapid urban growth. In theory, the residential locational choice model predicts that urban population can overshoot the socially
<!-- Start of picture text --> 8000<br>7000<br>—— Dar es Salaam<br>= 6000<br>S —— Nairobi<br>2. 5000 .<br>=> ——— Addis Ababa<br>S 4000 ? .<br>=I 7 = — -Antananarivo<br>= 3000 7<br>a 2 ——— Lusaka<br>@ 2000 o> —— Mombasa<br>1000 -<br>—— Maputo<br>fe}<br>1995 2005 2015 2025<br><!-- End of picture text -->
‘ae2014-16 (st‘<‘éi‘é‘éirir=i 0% 20% 40% 60% 80% 100% m Analamanga = Vakinankaratra m@ Haute Matsiatra mAtsinanana @Vatovavy Fitovinany Alaotra Mangoro @ Menabe Mitasy TM Bongolava @Atsimo Andrefana AtsimoAtsinanana mTM Boeny = Amoron | Mania ® Analanjirofo TM Betsiboka = lhorombe m Diana m Androy m Melaky mSofia mSava @ Anosy
<!-- Start of picture text --> o 140000 mmm Area occupied by road (ha) 35<br>£ F . ° eS<br>= 1 02 0000 -——=Share in built up areas(%) 3025 &8<br>>£ go000 20 Qa<br>2 60000 15 5<br>&= 4000020000 | 57-\ 510%3<br>3 eo [bitnns....--238~ 3<br>42 VSSOPSEASVEYoxcrnusSOpTCGSHUY AVTM=WSH=OGG VEV IGE OSSWO <o<br>2 gsogozstqessueloresSsshee<br>Ba38o SFA zaets2 GERM SE<br>a2 St5E=6 2 2E ®a goeeustsoS & 8= Beefc<br>g 7”<br><!-- End of picture text -->

<!-- Start of picture text --> oe<br>Rail, airport<br>Road 0.4%“ve (e) Rice field<br>1.5% -\ 27.2%<br>non<br>ne<br>Vegetable,<br>orchard,13.9%9 etc. erase Residential22.0%<br>15.7%<br><!-- End of picture text --> <!-- Start of picture text --> BN fy oe PET EAS SO<br>a1 na WO Ss ieee S 1 aE ee<br>NaS pe Mra LA | L<br>PEN ON a Ce ees<br>SnF o CieesSeal > , aeDas om y<br>Eyat:Bee a ores ICR EN SSOrcasONT RS) |<br>: AY ieee ayer Pe<br>POscauy 8S64. 2a<br>eets \ - skid onper ares Wiese oe\<br>Value40 yearHigh flood“4.007 (meter) 5\enyNex) La reemr Yi POYae VP VV eTPe\ 3 OY,rms<br>a OX Ces oo PRY,<br>B b 2 an mes KM<br>, fone. Sat DP bf<br><!-- End of picture text -->
<!-- Start of picture text --> ra. > _% } ies pot ji<br>oN Kf p f\ Oe / % Vand ~<br>yPCG = ‘a ye<br>ge} a a af Ve ~<br>Z4 ¥ > ae<br>ae We P | 7UL i<br>6 in@ 9 | .<br>ay aoO fae ; Yee<br>ae } Bas jo a<br>i J 4 Ht 5 t = > wa<br>hk \ Ie * N |<br>| @ LacAnosy (CBD) tee<br>a t Le) 7<br>—— RNS NA ix ov, ‘<br>4 —— Autre routes \ Ee we: -<br>< Pop density (per km2) ING ‘SS ba es<br> 131-383 AS! NN 4<br>384-650 : ~ > 4<br>©651 - 1077 . PS ee<br>{0 1078<br>MN 2079 -2078 ~ 7 |<br>Ml - 3494 4 vy of<br>MM 3495 - 6685 —— oY \<br>HBMMM 23879-521446686 13126 - 13125- 23878 N E ~ ay:aton 3 S3a;<br>MMM 52145-29150 SY 0 DS. Ve ns ‘<br><!-- End of picture text -->

<!-- Start of picture text --> 0.1-10.0 Ee Lo<br>ME 101-150 “\b ,<br>(MB 151-200 -<br>( 20.1-25.0<br>1) 251-300 PA y<br>J 301-350 ae . ae<br>SEANAD - aor on. ; a . i<br>MMM s01-55045.1-50.0 GaAaRSO RE ey seat aeae<br>55.1 -60.0 See * \ re pee he ae vie Rep .<br>B MM 1-00 0° an & me f ,<br>eis =i ‘ye. Ne 7. “4<br>: eae te <S<br>AR ’ Re fale 7<br>Bus traffic per day a . Ly A)<br>Bus sub-urbains a.Gan eet: ee LX Ts ct<br>—0-107 oh) . Sed. WF To OtyA a<br>— 108 - 285 ey mM eae ye<br>— 286 - 496 fi wfPe a Ga<br>comm= 799 497 -- 1410798 Wweey fisCiape¥hawes. +e)pele<br>City bus 4 ie fa ok \ y,<br>— 0-227 f CONS<br>——=—— 1465 228758 -- - 75714642623 w. N SheBi4 = Br<br>me 2624 - 6044 Y » |<br><!-- End of picture text -->
<!-- Start of picture text --> oe,<br><!-- End of picture text --> <!-- Start of picture text --> 14 ne nS a<br>oe, ——RN1 = Ari.imil<br>~ 13 —e—RN2<br>= 12 —=—RN3<br>> ——RN4<br>£<o41yw 10 ==RN7<br>Qa<br>3 (9<br>oO<br>28<br>7<br>0 5 10 15 20<br>Distance from CBD (km)<br>100% 100%<br>90% 90%<br>S& 80% SX 30%<br>g 70% 3 70%<br>© 60% @ 60%<br>me} me}<br>5 50% 5 50%<br>%5 40% %5 40%<br>£ 30% 2 30%<br>& 20% E 20%<br>10% 10%<br>0% 0%<br>1 3 5 7 9 11 13 15 17 19 21 23 25 1 3 5 7 9 11 13 15 17 19 21 23 25<br>Distance from CBD (km) Distance from CBD (km)<br><!-- End of picture text -->
- 8 -

facilities and other amenities (e.g., Orford, 2000; Tu et al., 2007; Jaeger, 2013; Gedal and Ellen, 2018). The current paper follows this approach: where _Vi_ is the land value at location _i_ . While _X_ is a variable representing connectivity to the CBD, _Z_ contain a set of locational characteristics observed.

13. In the literature, the connectivity _X_ is often defined by distance between a parcel of land and the CBD. Unlike earlier studies, the current paper uses more realistic variables: travel time estimates and the Job Accessibility Index (JAI). First, travel time is used, which is estimated by spatial software with road conditions and traffic taken into account. This is a more realistic measurement to capture the people’s actual accessibility to the CBD, because the accessibility can be different depending on actual traffic speed even if the distance is the same.

14. Second, the JAI is also used, which is considered to be even more comprehensive. Instead of measuring the distance or travel time to one particular place, i.e., the CBD, it takes into account the accessibility to multiple potential destinations. It is defined by the average size of economic activities or job opportunities inversely weighted by transport connectivity. The CBD is generally a good proxy of destination for many people, but where they actually go may differ: The CBD is only one of the destinations in reality. Therefore, the JAI is constructed based on the level of attractiveness of each destination and the transport connectivity from location _i_ to that destination. Formally, it is calculated by this: where _Ym_ represents the size of job opportunities at destination _m_ , which can be understood as the probability to visit this place. _dim_ is the connectivity between location _i_ and destination
- 9 -

_m_ . In our case, travel time is used for _dim_ . Then, the index is normalized to zero to one. Note that the higher _JAI_ means better connectivity, that is, location _i_ is closer to job opportunities.

15. To estimate Equation (1), one empirical issue is that spatial autocorrelation would likely matter to any locational data, such as land prices. This is often ignored in the existing literature. The land value at a particular place is likely to be related to its neighboring values because of unobserved neighboring factors and transport connectivity. Of particular note, transport infrastructure typically forms a network, which creates autocorrelation among neighboring area: Roads must be connected with each other.

16. One approach to mitigate such unobserved spatial autocorrelation is to introduce the location-specific fixed-effects in the model. For instance, Chen et al (2017) use the countyspecific fixed-effects to analyze the relationship between housing market thinness and proximity to the CBD in Maryland. In our case, unfortunately, panel data are not available.

17. Another approach to control for potential autocorrelation is the spatial autoregressive model (Meen, 1996; Orford, 2000; Tu et al., 2007), which the current paper relies on. That is, land value, _Vi_ , at a particular place, _i_ , is correlated to its neighboring values, _Vj_ . That is, Cov�u�, u���0.

where _w_ is an element of the spatial-weighting matrix. _λ_ and _ρ_ are spatial autoregressive parameters in the dependent variable and error term, respectively. _ε_ is an idiosyncratic error distributed independently and identically. Under the normality assumption, this can be estimated by the maximum likelihood estimation procedure (e.g., Anselin, 1988; Amaral and Anselin, 2011).<sup>1</sup>

> 1 For estimation we applied a STATA command **_spreg_** developed by Drukker, Prucha and Raciborski (2013).
- 10 -

18. For the spatial weighting matrix, inverse distances between two locations _i_ and _j_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other, if they are located closely. This follows Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things (Tobler 1970).”

# **IV. DATA**

19. Our unit of analysis is fokontany, which is the fourth-level of administrative boundaries, following regions, districts, and communes. In Greater Antananarivo, there are 556 fokontany in 4 districts. From the analysis, 8 observations are excluded, of which some data are missing. The summary statistics are shown in ( **Table 1** ). The average land prices were collected at each fokontany in 2018. The prices vary substantially between MGA 110 and MGA 1.3 million, with an average of about MGA 64,000 or US$18 per m<sup>2</sup> . Travel time is estimated using spatial software based on the road network data with road conditions and bus traffic attached (see **Figure 7** above). It takes 42 minutes on average, but from some fokontany, it may take more than an hour and a half.

20. As discussed, the JAI is calculated based on the size of job opportunities at each destination and the travel time between the origin and the destination. To identify and quantify the size of job opportunities, satellite imagery was used. The following areas were considered: Administrative Zone; Commerciale Zone; Industrial Zone; Cultural Facilities; Hotel Buildings; Market Area; and Office Buildings. In total, 1,094 land parcels were identified ( **Figure 11** ). The size of each parcel of land was measured as a proxy of the number of job opportunities.<sup>2</sup> It differs from 0.007 ha to 37 ha.

> 2 This was found to be a good proxy in our case: The measured land area is highly correlated with the number of registered businesses in the same area. Of course, some businesses may employ more people than others even though they occupy the same size of land. In general, however, there is strong correlation between them.
- 11 -

21. As discussed, the population distribution is highly skewed. The fokontany population density differs from 31 to 331,000 per km<sup>2</sup> . As locational attributes, travel time to the nearest health facility is calculated ( _CSB_ ). In Antananarivo, in general, people have good accessibility to health care services, but in suburban areas, the accessibility may be poorer ( **Figure 12** ). In addition, the climatic vulnerability, _VUL_ , is measured by the projected changes in travel time to the CBD when a 10-year flood happens, which is essentially assumed to reduce the average speed along affected roads. How much the speed would be reduced depends on flood depth.

22. Available transport space is also measured by the share of road space in total built-up areas, _RD_ . It is expected that greater road space would be desirable because traffic congestion might be eased. Moreover, the information is included on whether each fokontany is connected to the commune capital by all-weather roads during the rainy season. In Madagascar, the passability during the rainy season has been a matter of particular concern.

23. The impact of rice fields on land prices is also of particular interest. It is measured by the share of rice fields in the total fokontany land area, _RICE._ In some fokontany, a significant amount of land is used for rice production.

24. Proximity to public transportation is expected to be captured by two dummy variables: _TAXIBE_ and _SUBU_ are set to unity if there is a taxi-be and suburban bus route within 200m of fokontany (centroid). This threshold of the distance will be relaxed later in the analysis.

**<u>Table 1. Summary statistics</u>**

|<br>Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Land price (Ar/m2)|PRIX|548|64,308|172,984|110|1,298,500|
|Travel time to CBD (minutes)|MIN|548|42.20|21.42|2.41|98.07|
|Job Access Index (0 to 100)|JAI|548|19.83|11.52|0.01|100.00|
|Population density (per km2)|POPDEN|548|16,244|30,794|31|331,850|
|Travel time to the nearest basic health center|CSB|548|5.07|4.33|0.002|28.58|
|(minutes)|||||||
|Vulnerability (change in travel time to CBD under the|<br>VUL|548|1.20|3.68|0|43.40|
|flood scenario)|||||||
|Share of road areas in total built up areas|RD|548|0.06|0.05|0|0.36|
<!-- Start of picture text --> el ees \ Vm yy DS, fh<br>N ad i SR 4 SC 4 |<br>ee a axee<br>23 a) RO Ge<br>SON, ON<br>nar SY / Ae ica<br>71 ae ma en Sn »<br> 4,400 NCie < ey »;<br>=o > ea \ 4<br>oe , V7 y F 2<br>§ ad ry 7 _ A<br><!-- End of picture text -->

<!-- Start of picture text --> Say) a \ Pi a rae Ae<br>- “~ a<br>fie a7]ee :<br>hes Clee<br>Se Sis Neca Ge<br>ee ee es ie f<br>a oie = ry. ie<br>00-18 el % te atop<br>19-28 Lii KR Sake<br>ee 83-339 W 4 So th<br>,)<br><!-- End of picture text -->
- 13 -

# **V. ESTIMATION RESULTS**

25. First, OLS regression is performed ( **Table 2** ). Although the results may not be unbiased because of the possible autocorrelation across the observations, the estimated coefficients are broadly consistent with our prior expectation: The land prices decrease with the distance from the CBD and increase with the job accessibility. Better connected places tend to be more expensive.

26. To control for the autocorrelation, the spatial autoregressive model is used. The results are shown in **Table 3** . The results are broadly similar, but the spatial autoregressive coefficients, _λ_ and _ρ_ , are both significantly positive, confirming that autocorrelation does matter to our data. Therefore, the OLS results are likely inconsistent. The following discussion is concentrated on the results based on the spatial autoregressive models.

27. As expected, the land value gradient is negative: The coefficient of travel time to the CBD is estimated at -0.402, which is statistically significant. On the other hand, the estimated gradient is also found to be significant when the JAI is used as a connectivity measurement. The coefficient is 0.122, meaning that the land value would increase by 1.2 percent if the job accessibility is improved by 10 percent. When both are included in the model, the travel time variable seems to dominate the JAI.

28. Regardless of whether the connectivity is measured by travel time or JAI, the estimated land price gradients are within the range of the estimates in the literature but look relatively steep. For example, In New York City, the elasticity is estimated at -0.65 to -0.04, depending on the time period. It tends to be declining over time (Atack and Margo, 1998). According to Gedal and Ellen (2018), it is estimated at -0.06 to -0.1. In China, it was estimated at about - 0.05 for residential areas and -0.01 for commercial areas (Qin et al., 2016). Policy implications are clear: The land and housing prices in the middle of the city tend to overshoot, pushing the poor away to suburban areas. The housing affordability and the lack of transport connectivity are two important policy issues to be addressed.
- 14 -

29. Road space is found to be an important determinant of land value. The more road space there is, the higher is land value. This is understood to mean that people value transport infrastructure because of less traffic congestion and better mobility, and possibly as an element of spatial amenity. In fact, amenity areas, such as cultural facilities, commercial zones, hotels and restaurants, are preferred: The coefficient of _AMENITY_ is significantly positive.

30. All-weather connectivity to the commune capital is also important. The coefficient of _ALLWEARD_ is always significantly positive. This is another piece of evidence supporting the importance of transport infrastructure for land values. Access to taxi-be services is also likely to contribute to increase land values. It is interpreted to mean that taxi-be is an essential transport means for people, which is consistent with and reflects the reality in Antananarivo.

31. With respect to other coefficients, not surprisingly, flood-prone areas are undesirable: The vulnerability has a negative impact on land prices. The coefficient is found to be always significantly negative. On the other hand, the impact of rice fields looks inconclusive. The coefficient is found to be negative as expected, but statistically insignificant. Thus, the land prices may be underestimated because of the presence of massive rice fields in the city. But further investigation is needed to determine the implication of possible changes in land use.

**<u>Table 2. OLS estimation results</u>**

||Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>|Std.Err.|
|---|---|---|---|---|---|---|
|ln_MIN_|-0.314|<br>(0.118) ***|||-0.283|(0.134) **|
|ln_JAI_|||0.131|<br>(0.048) ***|0.063|(0.063)|
|ln_POPDEN_|0.111|<br>(0.034) ***|0.123|<br>(0.032) ***|0.101|(0.034) ***|
|ln_CSB_|-0.049|<br>(0.036)|<sup>-0.045</sup>|<br>(0.037)|<sup>-0.046</sup>|(0.037)|
|_VUL_|-0.048|<br>(0.007) ***|-0.047|<br>(0.007) ***|-0.048|(0.007) ***|
|_RD_|2.923|<br>(0.717) ***|3.086|<br>(0.774) ***|2.852|(0.717) ***|
|_ALLWEARD_|0.214|<br>(0.072) ***|0.249|<br>(0.065) ***|0.209|(0.071) ***|
|_RICE_|-0.121|<br>(0.153)|<sup>-0.168</sup>|<br>(0.160)|<sup>-0.158</sup>|(0.157)|
|_AMENITY_|2.270|<br>(1.388) *|2.731|<br>(1.489) *|2.312|(1.392) *|
|_TAXIBE_|-0.004|<br>(0.082)|0.019|<br>(0.084)|<sup>-0.012</sup>|(0.083)|
|_SUBU_|-0.003|<br>(0.069)|<sup>-0.030</sup>|<br>(0.070)|<sup>-0.010</sup>|(0.070)|
|Constant|9.408|<br>(0.659) ***|7.719|<br>(0.279) ***|9.209|(0.759) ***|
- 15 -

|Obs.|548|548|548|
|---|---|---|---|
|No. of district||||
|dummies|3|3|3|
|R-squared|0.674|0.670|0.675|
|F-statistic|76.31|74.82|70.99|

**<u>Table 3. Spatial autoregressive estimation results</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|ln_MIN_|-0.402|(0.131) ***|||-0.329|(0.141) **|
|ln_JAI_|||0.122|(0.061) **|0.093|(0.067)|
|ln_POPDEN_|0.044|(0.030)|0.034|(0.030)|0.036|(0.030)|
|ln_CSB_|-0.037|(0.028)|<sup>-0.034</sup>|<sup>(0.028)</sup>|<sup>-0.033</sup>|<sup>(0.028)</sup>|
|_VUL_|-0.024|(0.008) ***|-0.022|(0.008) ***|-0.024|(0.008) ***|
|_RD_|1.8208|0.562 ***|1.8744|0.5598 ***|1.7532|0.5632 ***|
|_ALLWEARD_|0.189|(0.070) ***|0.171|(0.070) **|0.180|(0.070) **|
|_RICE_|-0.151|(0.159)|<sup>-0.220</sup>|<sup>(0.158)</sup>|<sup>-0.184</sup>|<sup>(0.160)</sup>|
|_AMENITY_|1.271|(0.787) *|1.707|(0.782) **|1.283|(0.786) *|
|_TAXIBE_|0.187|(0.087) **|0.169|(0.087) *|0.182|(0.087) **|
|_SUBU_|-0.034|(0.058)|<sup>-0.039</sup>|<sup>(0.059)</sup>|<sup>-0.041</sup>|<sup>(0.059)</sup>|
|Constant|1.509|(2.051)|<sup>-12.508</sup>|<sup>(2.634)***</sup>|1.254|(2.216)|
|Obs.|548||548||548||
|No. of district|||||||
|dummies|3||3||3||
|Wald chi2|181.15||164.45||184.97||
|Spatial parameters:|||||||
|_λ_|0.801|(0.193) ***|2.010|(0.260) ***|0.783|(0.210) ***|
|_ρ_|2.772|(0.123)***|2.416|(0.054)***|2.786|(0.125)***|

# **VI. DISCUSSION**

32. One may be concerned about the robustness of the above estimation results. In particular, the definition of bus service access was an ad hoc assumption in the above. To address this issue, the accessibility to bus services was redefined using a different threshold, i.e., 500 meters. The results are shown in the first two columns of **Table 4** . The statistical significance associated with access to taxi-be routes disappeared, while the rest of the coefficients remain broadly unchanged. It can be understood to mean that the close proximity to taxi-be services is particularly important to local people. The 500-meter distance seems too long, suggesting that an extensive bus network needs to be developed to meet the people’s needs, which are reflected in the observed land values.
- 16 -

33. In addition, the bus access variables can be replaced with the direct measurements of the distance to a bus route. The results are presented in the last two columns of the table. Both distance variables are found to have negative coefficients as expected. That is, as one moves away from a bus route, the land value would likely decline. But there is no statistical significance, except for one: In the last column model, the land value is found to decrease significantly with the distance to the nearest taxi-be route. The coefficient is estimated at - 0.038, meaning that a 10 percent increase in distance to taxi-be routes would reduce the land price by about 0.4 percent. All the indications are that the proximity to bus services is an important element of land value in Antananarivo.

**<u>Table 4. Spatial autoregressive models with alternative bus accessibility variables</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|---|---|
|ln_MIN_|-0.062|(0.133)|||-0.350|(0.124) ***|||
|ln_JAI_|||0.106|(0.064) *|||0.094|(0.065)|
|ln_POPDEN_|0.029|(0.030)|0.033|(0.030)|0.021|(0.030)|0.029|(0.030)|
|ln_CSB_|-0.040|(0.028)|-0.031|(0.028)|-0.033|(0.028)|-0.032|(0.028)|
|_VUL_|-0.023|(0.008) ***|-0.023|(0.008) ***|-0.024|(0.008) ***|-0.023|(0.008) ***|
|_RD_|1.8513|0.5606 ***|1.8443|<br>0.555 ***|1.7186|0.5552 ***|1.7953|0.5565 ***|
|_ALLWEARD_|0.137|(0.069) **|0.163|(0.068) **|0.145|(0.071) **|0.143|(0.071) **|
|_RICE_|-0.076|(0.162)|<sup>-0.183</sup>|<sup>(0.160)</sup>|<sup>-0.230</sup>|<sup>(0.157)</sup>|<sup>-0.198</sup>|<sup>(0.160)</sup>|
|_AMENITY_|1.476|(0.784) *|1.810|(0.777) **|1.635|(0.784) **|1.859|(0.779) **|
|_TAXIBE500_|0.087|(0.101)|0.116|(0.097)|||||
|_SUBU500_|0.084|(0.078)|0.067|(0.079)|||||
|ln_KMTAXIBE_|||||-0.034|(0.022)|<sup>-0.038</sup>|<sup>(0.023) *</sup>|
|ln_KMSUBU_|||||-0.014|(0.016)|<sup>-0.012</sup>|<sup>(0.016)</sup>|
|Constant|-2.756|(1.431)*|-11.519|(3.115)***|-9.968|(2.926)***|-11.281|(3.148)***|
|Obs.|548||548||548||548||
|No. of district|||||||||
|dummies|3||3||3||3||
|Wald chi2|134.05||165.52||169.57||167.12||
|Spatial parameters:|||||||||
|_λ_|1.132|(0.124) ***|1.909|(0.306) ***|1.922|(0.276) ***|1.896|(0.309) ***|
|_ρ_|3.406|(0.178)***|2.673|(0.098)***|2.424|(0.047)***|2.678|(0.097)***|

34. Another matter of concern may be endogeneity. Especially, people’s locational choice is potentially endogenous with land prices. While the land prices increase where many people move in, people may refrain from living in the places where land prices are high. Thus, the
- 17 -

population density could be correlated with the error term in the equation. To test this possibility, the spatial autoregressive instrumental variable (SPIV) model was performed. Taking advantage of lagged variables is one approach. The population density in 2007 is used as an instrument in this model.

35. As shown in **Table 5** , the results are broadly unchanged. The Hausman endogeneity test statistics indicate that the SPIV models are better. The test statistics are estimated at 8.68 and 16.51, which can reject the hypothesis of no autocorrelation. Based on the SPIV estimation, the land prices are found to decrease with the accessibility to jobs (JAI). The elasticity is 0.108. The other coefficients are broadly similar to the above results: The access to taxi-be is important. Road infrastructure and all-weather road connectivity are also found to be essential. While proximity to amenity zones is appreciated, the areas that are vulnerable to flood are not preferred.

**<u>Table 5. Spatial autoregressive IV estimation results</u>**

||<br>Coef.|<br>Std.Err.||<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_MIN_|-0.131|(0.132)|||||
|ln_JAI_||||0.108|(0.065)|*|
|ln_POPDEN_07|0.063|(0.037)|*|0.049|(0.037)||
|ln_CSB_|-0.031|(0.029)||-0.030|(0.029)||
|_VUL_|-0.025|(0.008)|***|-0.024|(0.008)|***|
|_RD_|1.911|0.5694|***|1.848|0.5678|***|
|_ALLWEARD_|0.149|(0.071)|**|0.152|(0.071)|**|
|_RICE_|-0.019|(0.163)||-0.080|(0.164)||
|_AMENITY_|1.552|(0.782)|**|1.558|(0.771)|**|
|_TAXIBE_|0.142|(0.087)|*|0.157|(0.087)|*|
|_SUBU_|-0.027|(0.059)||-0.037|(0.059)||
|Constant|6.938|(3.825)|*|5.152|(3.222)||
|Obs.|548|||548|||
|No. of district|||||||
|dummies|3|||3|||
|Wald chi2|139.05|||142.39|||
|Spatial parameters:|||||||
|_λ_|0.187|(0.356)||0.290|(0.329)||
|_ρ_|3.549|(0.708)|***|3.453|(0.746)|***|
|Hausman endogeneity<br>Chi2 test statistic|test:<br>8.680|||16.510|||
- 18 -

# **VII. CONCLUSION**

36. Understanding how land prices are determined is of particular importance for policy makers; however, there is little evidence in the developing world, particularly in Africa. Many African countries are currently experiencing very rapid urbanization. The paper examined the relationship between land prices and locational characteristics using data from Antananarivo, the capital of Madagascar, where over 2.6 million people live.

37. It was found that the land prices in Antananarivo are much elevated in the center of the city and taper off quickly toward suburban areas. Since spatial data, such as land prices, are normally autocorrelated, the spatial autoregressive model was applied. The land value gradients were found to be relatively steep. The elasticity is -0.402 with respect to travel time to the CBD and 0.122 with respect to the accessibility to jobs, i.e., JAI. This confirms the facts that the land and housing prices in the middle of the city overshoot and push the poor away from the city to suburban areas, because they are not affordable.

38. The results also indicate that transport infrastructure and services, such as taxi-be and suburban bus systems, are underdeveloped, restricting people’s mobility. With wider suburban development, the land price gradients would be less steep. In particular, our estimation results suggest that an extensive bus network needs to be developed to meet the people’s needs. The proximity to taxi-be services was always found to be an important determinant of land value in Antananarivo.

39. Not only transport connectivity but also other factors, such as proximity to amenities and administrative centers, were also found to be important to determine the land values. The climate vulnerability also has a negative impact on land prices. These elements cannot be ignored to stimulate urban development in Antananarivo. On the other hand, the impact of rice fields looks inconclusive.