scholarly journals Research of the Critical Capitalization Rate in Building Damage Appraisal

2022 ◽  
Vol 14 (1) ◽  
pp. 486
Author(s):  
Serena Artese ◽  
Manuela De Ruggiero ◽  
Francesca Salvo ◽  
Raffaele Zinno

From the perspective of building health monitoring and property management, this research proposes some parametric measures of the capitalization rate, in order to define a range of significant values to be used in a cash flow analysis intended for monetary evaluation in partial building damage assessment. If criteria and methods for appraising partial damage to buildings are widely shared in the scientific and professional communities, the identification of the most appropriate capitalization rate is rather more controversial, and certainly more complex. The proposed approach borrows the logical principles of cash flow analysis based on the yield capitalization approach, considering both recovery costs and loss of incomes when building partial damage occurs. The procedure is a differential valuation that considers a situation before and a situation after the damage, basing on the cost approach and the income approach. In particular, two distinct conditions are considered: the case of recovery interventions and that of improvement.

1993 ◽  
Vol 24 (4) ◽  
pp. 130-133
Author(s):  
S. Paulo

The purpose of this technical note is to draw attention to the problems which are inherent in the use of certainty equivalent coefficients as an approach to incorporating risk into capital budgeting. More specifically, the certainty equivalent coefficient net present value criterion violates an important principle of cash flow determination for discounted cash flow analysis. Further, this approach precludes the use of net present value profiles which are pivotal when evaluating conflicts among mutually exclusive projects. In addition, use of certainty coefficient equivalents amounts to an acknowledgement that the concept, function and use of the cost of capital is improperly understood.


1997 ◽  
Author(s):  
Bruce G. Hansen ◽  
A. Jeff Palmer

2018 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Halkadri Fitra ◽  
Salma Taqwa ◽  
Charoline Cheisviyanny ◽  
Abel Tasman ◽  
Nurzi Sebrina

Penelitian ini bertujuan untuk melihat kelayakan aspek keuangan usaha grosir sembako Badan Usaha Milik Desa (Nagari) Kamang Hilia Sejahtera di Kenagarian Kamang Hilia Kecamatan Kamang Magek Kabupaten Agam Provinsi Sumatera Barat yang dilakukan pada tahun 2018. Penelitian bersifat deskriptif kuantitatif dengan menggunakan metode cash flow analysis, payback period, net present value, profitability index, internal rate of return, dan average rate of return. Hasil penelitian menunjukkan bahwa nilai net cash flow Badan Usaha Milik Desa (Nagari) Kamang Hilia Sejahtera adalah positif yaitu Rp.21.774.000, nilai payback period adalah 1,15 tahun, nilai net present value positif sebesar Rp.10.680.034,47, nilai profitability index adalah positif 1,37, sedangkan nilai internal rate of return adalah 46,7% dan nilai average rate of return adalah 57,23%. Berdasarkan standar penilaian maka semua metode yang digunakan memberikan kesimpulan bahwa usaha grosir sembako milik Badan Usaha Milik Desa (Nagari) Kamang Hilia Sejahtera dalam kategori layak untuk dilaksanakan.


2017 ◽  
Vol 30 (1) ◽  
pp. 83-89
Author(s):  
E. S. Epifanov ◽  
N. Z. Atarov

This article presents a description of the main methods of valuation Internet business: a comparative approach, the income approach, the cost approach; and provides guidance on the valuation of Internet projects.


2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.


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