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Disaggregate Hierarchical Decision Huff Model Incorporating Consumer Kaiyu Choices Among Shopping Sites Read chapter Read first chapter

2023 | OriginalPaper | Chapter

A Disaggregate Kaiyu Markov Model to Forecast the Sales of Retail Establishments Based on the Consumers’ Frequency of Visits

Authors : Kenichi Ishibashi, Saburo Saito

Published in: Recent Advances in Modeling and Forecasting Kaiyu

Publisher: Springer Nature Singapore

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Abstract

We have already proposed the frequency-based consumer Kaiyu Markov model to evaluate redevelopment programs in a city center retail environment from the viewpoint of consumers’ Kaiyu behaviors (The model referred to is the one proposed by Saburo Saito, Yoshinobu Kumata, and Kenichi Ishibashi, “A choice-based Poisson regression model to forecast the number of shoppers: Its application to evaluating changes of the number and shop-around pattern of shoppers after city center redevelopment at Kitakyushu City,” Papers on City Planning 30, 1995). This frequency-based model was an excellent one in the sense that the model enables one to account for the combined effects of the increase of attracted visitors and the change of consumers’ Kaiyu movements caused by redevelopment in terms of how many consumers visit and move among retail facilities. However, redevelopment programs require a significant investment, so the frequency-based Kaiyu Markov model needs to incorporate a money-based retail sales forecast. This study aims to construct such a model by disaggregating and introducing a consumer’s purchasing model in the previous model and show it with its application to the case of Kitakyushu City.

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previous chapter Estimation of Disaggregate Huff and Kaiyu Markov Model: A Lecture Note on Conditional Logit Model
next chapter How Would the Kyushu Super-Express Railway Opening Change the Flow of Tourists from the Kansai Region within the Kyushu Wide Area, Japan?: A Micro Behavior Analysis of the Destination’s Hub Function
Footnotes
1
Daiten Ho in Japanese.
 
2
Shogyo Shuseki Ho in Japanese.
 
3
Chushin Shigaichi Kasseika Ho in Japanese.
 
4
Daiten Ricchi Ho in Japanese.
 
5
The US blamed the Large-Scale Retail Store Law as a typical non-tariff barrier and demanded to abolish it from the beginning of US-Japan Structural Impediment Initiatives. Finally, after several amendments, the Japanese Government scheduled to repeal it in 2000 at the time of enforcement of the Large-Scale Retail Store Location Law enacted in 1998.
 
6
Kitakyushu City is an ordinance-designated city with a population of 961,286, according to the 2015 census. It stands at the northern part of Kyushu Island, the most south island among the four islands composing Japan. It once was famous for its big steel industry and is now known as an environmental model city seeking a culturally creative city.
 
7
While Saito [4] developed his Markov shop-around model without notice of Sasaki’s work [22], these two models of Markov chain are essentially the same. However, Saito’s motivation to evaluate the spatial structure of CCRE is quite different from that of Sasaki’s.
 
8
Also refer to Saito et al. [6], Chapter 4, “Basics of Kaiyu Markov Models: Reproducibility Theorems—A Validation of Infinite Kaiyu Representation,” in this volume for basics for the Kaiyu Markov model and its recent developments.
 
9
Also refer to Saito and Sakamoto [8], Chapter 3, “Kaiyu Markov model and evaluation of retail spatial structures,” in this volume.
 
10
Also refer to Saito and Motomura [16].
 
11
Also refer to Saito and Ishibashi [19], Chapter 5, “Kaiyu Markov Model with Covariates to Forecast the Change of Consumer Kaiyu Behaviors Caused by a Large-Scale City Center Retail Redevelopment,” in this volume. For fundamentals of the estimation of the Kaiyu Markov model with covariates, refer to Saito et al. [20], Chapter 6, “Estimation of Disaggregate Huff and Kaiyu Markov Model: A Lecture Note on Conditional Logit Model,” in this volume.
 
12
For the more recent review up to 2007, refer to Yamaguchi and Saito [24]. Also refer to Yamaguchi and Saito [25], Chapter 14, “A Bayesian Network Model of Consumers’ Kaiyu Behaviors” in this volume.
 
13
Refer to Saito and Ishibashi [19], Chapter 5, “Kaiyu Markov Model with Covariates to Forecast the Change of Consumer Kaiyu Behaviors Caused by a Large-Scale City Center Retail Redevelopment,” in this volume.
 
14
Another reason might be the theoretical problem of dealing with many zeroes (not purchased steps) in the data on the purchase amount at each step of shop-arounds.
 
15
For a more complete exposition of the Kaiyu Markov model, refer to Saito et al. [6], “Basics of Kaiyu Markov models: Reproducibility theorems—a validation of infinite Kaiyu representation,” Chapter 4 in this volume.
 
16
While not necessarily limited, the number of the elements in the set H is supposed to be one hereafter, which will be directly related to the model estimation in the application.
 
17
The purchase model for expenditure per visit assumes that the expenditure is independent of Kaiyu’s steps. However, why does the Kaiyu multiple or shop-around effect differ here regarding visits and money? There might be some causal routes in which the individual attributes of consumers may affect the shop-around choice or Kaiyu choice probabilities and, consequently, the expected length of the shop-around or Kaiyu paths leading to the differences in expenditure depending on the stage in the Kaiyu steps.
 
18
We can obtain these figures by the same calculation shown in the bottom row in Table 9.
 
19
(49,173 (No. 3 and the rightmost column) + 61,921 (No. 28 and the rightmost column))/401,587 (Total’s row and the rightmost column) = 0.2766.
 
20
Referring to the row of No. 28, 12,409/49,512 = 0.2506.
 
21
How consumers spend as their Kaiyu steps proceed is almost unknown. For example, their spending is decaying or increasing or showing any other patterns on average as the Kaiyu process proceeds. Further, how we make an accurate guess at whether they buy or not at a specific shop is unknown either.
 
22
The original version of this chapter first appeared in Ishibashi et al. [30].
 
Literature
1.
Saito, S., & Ishibashi, K. (1992). Forecasting consumer’s shop-around behaviors within a city center retail environment after its redevelopments using Markov chain model with covariates. Papers on City Planning, 27, 439–444 (in Japanese).
2.
Saito, S., Kumata, Y., & Ishibashi, K. (1995). A choice-based Poisson regression model to forecast the number of shoppers: Its application to evaluating changes of the number and shop-around pattern of shoppers after city center redevelopment at Kitakyushu City. Papers on City Planning, 30, 523–528. (in Japanese).
3.
Saito, S., Kumata, Y., & Ishibashi, K. (1996). A choice-based Poisson regression model: Its integrated use with Markov shop-around model to evaluate city center retail redevelopment. Paper presented at the Third International Conference on Recent Advances in Retailing and Service Science, Telfs/Buchen, Austria.
4.
Saito, S. (1983) Present situation and challenges for the commercial districts in Nobeoka Area. In The report of regional plan for modernizing commerce: Nobeoka Area. Committee for Modernizing Commerce Nobeoka Region Section (pp. 37–96).
5.
Sasaki, T. (1971). Estimation of person trip patterns through Markov chains. In G. F. Newell (Ed.), Traffic flow and transportation. Elsevier.
6.
Saito, S., Ishibashi, K., Yamashiro, K., & Iwami, M. (2023). Basics of Kaiyu Markov models: Reproducibility theorems—a validation of infinite Kaiyu representation (Chapter 4 in this volume). Springer.
7.
Sakamoto, T. (1984). An absorbing Markov chain model for estimating consumers’ shop-around effect on shopping districts. Papers on City Planning, 19, 294–329. (in Japanese).
8.
Saito, S., & Sakamoto, T. (2023). Kaiyu Markov model and evaluation of retail spatial structures (Chapter 3 in this volume). Springer.
9.
Saito, S. (1984). A survey report on the modified Huff model: The development of SCOPES (Saga Commercial Policy Evaluation System). Saga City Government. (in Japanese).
10.
Saito, S. (1984). A disaggregate hierarchical Huff model with considering consumer’s shop-around choice among commercial districts: Developing SCOPES (Saga Commercial Policy Evaluation System). Planning and Public Management, 13, 73–82. (in Japanese).
11.
Saito, S. (1986). Analysis report of Saga citizen opinion survey: city attractiveness and policy demand of Saga City. Saga City Government. (in Japanese).
12.
Saito, S. (1988). Assessing the space structure of central shopping district viewed from consumers’ shop-around behaviors: A case study of the midtown district of Saga City. Fukuoka University Economic Review, 33(1), 47–108. (in Japanese).
13.
Saito, S. (1988). Duration and order of purpose transition occurred in the shop-around trip chain at a midtown district. Papers on City Planning, 23, 55–60. (in Japanese).
14.
Saburo, S. (1993). Report on evaluating urban development plan at the area around the Murasaki River from the viewpoint of pedestrian Kaiyu behavior. Mimeo (in Japanese).
15.
Saito, S., Sakamoto, T., Motomura, H., & Yamaguchi, S. (1989). Parametric and non-parametric estimation of distribution of consumer’s shop-around distance at a midtown district. Papers on City Planning, 24, 571–576. (in Japanese).
16.
17.
Saito, S., & Ishibashi, K. (1992). A Markov chain model with covariates to forecast consumer’s shopping trip chains within a central commercial district. Paper presented at the Fourth World Congress of the Regional Science Association International, Palma de Mallorca, Spain, May 26–29, 1992.
18.
Saburo, S., & Ishibashi, K. (1993). Evaluating redevelopment plans based on the forecasts of the number of visitors by the Poisson regression model for choice-based sample. In S. Saito (Ed.), Report on evaluating urban development plan at the area around the Murasaki River from the viewpoint of pedestrian Kaiyu behavior (in Japanese).
19.
Saito, S., & Ishibashi, K. (2023). Kaiyu Markov model with covariates to forecast the change of consumer Kaiyu behaviors caused by a large-scale city center retail redevelopment (Chapter 5 in this volume). Springer.
20.
Saito, S., Yamashiro, K., & Iwami, M. (2023). Estimation of disaggregate Huff and Kaiyu Markov model: A lecture note on conditional logit model (Chapter 6 in this volume). Springer
21.
Saito, S., Kakoi, M., & Nakashima, T. (1999). On-site Poisson regression modeling for forecasting the number of visitors to city center retail environment and its evaluation. Studies in Regional Science, 29(1), 55–74. CrossRef
22.
Saito, S., Nakashima, T., & Kakoi, M. (1999). Identifying the effect of city center retail redevelopment on consumer’s shop-around behavior: An empirical study on structural changes of city center at Fukuoka City. Studies in Regional Science, 29(3), 107–130. (in Japanese). CrossRef
23.
Ishibashi K (1998) A study on evaluation model for development program in central commercial district using consumer’s shop-around behavior. Doctoral Dissertation, Tokyo Institute of Technology
24.
Yamaguchi, R., & Saito, S. (2007). Bayesian networks for probabilistic inference of complex consumer shop-around behavior. Studies in Regional Science, 37(3), 769–785. (in Japanese). CrossRef
25.
Yamaguchi, R., & Saito, S. (2023). A Bayesian network model of consumers’ Kaiyu behaviors (Chapter 14 in this volume). Springer
26.
Huff, D. L. (1963). A probabilistic analysis of shopping center trade areas. Land Economics, 39(1), 81–90. CrossRef
27.
Huff, D. L. (1964). Defining and estimating a trading area. Journal of Marketing, 28(3), 34–38. CrossRef
28.
Ishibashi, K., Saito, S., & Kumata, Y. (1998). A disaggregate markov shop-around model to forecast sales of retail establishments based on the frequency of shoppers’ visits: Its application to city center retail environment at Kitakyusyu city. Papers on City Planning, 33, 349–354. (in Japanese).
29.
Kumagai, Y. (1973). Formation of agglomeration. In S. Ishihara (Ed.), Urban social system (pp. 22–46). The Nikkan Kogyo ShimbunSha. (in Japanese).
30.
Ishibashi, K., Saito, S., & Kumata, Y. (1998). Forecasting sales of shopping sites by use of the frequency-based disaggregate Markov shop-around model of consumers: Its application to the central commercial district at Kitakyushu City. Paper presented at the 5th Summer Institute of the Pacific Regional Science Conference Organization (PRSCO), Nagoya, Japan.
Metadata
Title
A Disaggregate Kaiyu Markov Model to Forecast the Sales of Retail Establishments Based on the Consumers’ Frequency of Visits
Authors
Kenichi Ishibashi
Saburo Saito
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Recent Advances in Modeling and Forecasting Kaiyu

Print ISBN: 978-981-9912-40-7

Electronic ISBN: 978-981-9912-41-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-1241-4_7