An Examination of Charges for Mobile Network Elements in Israel

Transcription

1 August 24, 2010 An Examination of Charges for Mobile Network Elements in Israel Reply to Consultation Responses Prepared for the Israeli Ministry of Communications and the Ministry of Finance Public Version

2 NERA Economic Consulting 1 Front St., Suite 2600 San Francisco, California Tel: Fax: NERA Economic Consulting 15 Stratford Place London W1C 1BE United Kingdom Tel: Fax:

3 Contents 1. INTRODUCTION THE MODEL RESULTS Voice Termination SMS Termination MMS Termination Data Termination Recommendation Regarding Termination Rates Symmetric rate Network externalities and other mark-ups Glide paths NEW DATA AND MODEL REVISIONS Summary of Model Revisions Operational Expenditures New data for site opex New data on site opex trend New data on other network equipment opex Spectrum and Licence Fees G spectrum and licence fees G spectrum and licence fees Network Dimensioning G carrier capacity Daily traffic profile Treatment of 3G voice and data Allocation of costs according to busy hour or total traffic Bouncing busy hour Backhaul and Core Transmission BTS-BSC/Node B-RNC links Core network transmission Demand Population Penetration rates and data usage On-net/off-net traffic balance Other Issues Traffic data Royalties Working capital WACC Capex IP network costs Cell radii Common costs...29 NERA Economic Consulting 1

4 3.8. Reasonableness checks of the model results Equipment quantities Aggregate costs Other reasonableness checks...30 APPENDIX A. APPENDIX B. APPENDIX C. APPENDIX D. MODEL SENSITIVITY ANALYSES...32 RESPONSES TO SPECIFIC POINTS RAISED BY CELLCOM...34 RESPONSES TO SPECIFIC POINTS RAISED BY PARTNER...35 RESPONSES TO SPECIFIC POINTS RAISED BY PELEPHONE...36 NERA Economic Consulting 2

5 1. Introduction NERA Economic Consulting (NERA) was commissioned by the Israeli Ministry of Communications (MOC) and Ministry of Finance (MOF) to construct a bottom-up cost model to examine the charges for network elements in the mobile telephony market in Israel and, if appropriate, recommend changes to the regulated current interconnection rates. In line with international best practice and a previous cost model constructed for the MOC and the MOF, we constructed a draft bottom-up LRIC model. In deriving the input values for this model, we solicited specific data from the Israeli operators, and also requested market level data from the MOC. Although we received some data from the operators, for many necessary inputs to the draft model no information was provided and consequently we had to rely on international benchmark data, forecasts, or commercially and publicly available data sources. The publicly available data sources included operator annual reports and Form 20-Fs, and indeed these are referenced in our draft report. However, such publicly available sources do not contain sufficient information to produce a LRIC model. Such sources include financial data at an aggregate level but it is not possible to identify total network costs to any reasonable degree of accuracy using these sources. They certainly do not provide information on the prices of different types of network equipment or the operating costs associated with them. None of the technical network engineering information required in a LRIC model can be found in annual reports or Form 20-Fs. Nevertheless, when building the draft model we made a conscious effort to use all the data received from the Israeli operators unless we found the data to be inconsistent with international benchmarks. The draft model underwent a consultation process, and the operators provided a variety of responses. Significant information that has been incorporated into the updated model was produced which had not previously been provided to us even though it was expressly requested in our data request. In response to the new data and comments received on the draft model, we have made appropriate changes to the model inputs and calculations in a revised model. In various instances errors were claimed to have been found in the draft model which upon investigation were found not to be the case. These are noted in the detailed explanations below. In the summary table below, we present the voice termination costs and proposed prices from We recommend that the MOC set prices at average blended cost, as shown in the table below. We further recommend that all termination rates be symmetric in that the same rate would apply for all networks regardless of whether the network is 2G or 3G. NERA Economic Consulting 3

6 Summary Recommendation Israeli Termination Rates (real 2009 ILS agorot and excluding royalties) Voice cost (per minute) Voice price (per minute) SMS cost (per minute) SMS price (per minute) Source: NERA. The remainder of this report is structured as follows: Section 2 presents the revised model results in full and details of NERA s recommendation regarding the prices for mobile termination; Section 3 explains the changes we have made in the revised model and its inputs; Appendix A contains sensitivity analysis; and Appendices B, C and D respond to each point raised by Cellcom, Partner and Pelephone respectively in their submissions during the consultation process. NERA Economic Consulting 4

7 2. The Model Results Based on the changes we have made in the revised model following comments on the draft model and the large volume of new data received, we recommend calculating the LRIC costs for the three operators using the following model settings: Depreciation method: Tilted straight line Special coverage: FALSE Use Gigabit Ethernet (10 GbE): FALSE Leased line discount: 0% MNO specific backhaul (BTS): FALSE MNO specific backhaul (Others): FALSE Bouncing busy hour uplift: TRUE NERA HSPA carrier capacity: FALSE Common carrier resources: TRUE Based on these settings and the given input values, the model produces LRIC costs for voice termination, SMS, MMS, and data termination for Cellcom, Partner, and Pelephone from These are presented in 2009 prices, excluding royalties Voice Termination The LRIC estimates for voice termination for Cellcom, Partner, and Pelephone are shown below. Also included is the simple average cost of the three modeled operators. NERA Economic Consulting 5

8 Figure 2.1 NERA BU-LRIC Model Results Per Minute Voice Termination 2G Average Agorots per Minute Average Figure 2.2 NERA BU-LRIC Model Results Per Minute Voice Termination 3G Average Agorots per Minute Average NERA Economic Consulting 6

9 Table 2.1 NERA BU-LRIC Model Results Per-Minute Voice Termination Voice Termination, per minute costs 2G Cellcom Partner Pelephone Average G Cellcom Partner Pelephone Average SMS Termination The LRIC estimates for SMS termination for Cellcom, Partner, and Pelephone are shown below. The figures and table also show the simple average cost of the three modeled operators. Figure 2.3 NERA BU-LRIC Model Results Per SMS Termination 2G Agorots per SMS Average NERA Economic Consulting 7

10 Figure 2.4 NERA BU-LRIC Model Results Per SMS Termination 3G Agorots per SMS Average Table 2.2 NERA BU-LRIC Model Results Per SMS Termination SMS Termination, per message costs 2G Cellcom Partner Pelephone Average G Cellcom Partner Pelephone Average MMS Termination The LRIC estimates for MMS termination for Cellcom, Partner, and Pelephone are shown below. NERA Economic Consulting 8

11 Figure 2.5 NERA BU-LRIC Model Results Per MMS Termination 2G Average Agorots per MMS Average Figure 2.6 NERA BU-LRIC Model Results Per MMS Termination 3G Average Agorots per MMS Average NERA Economic Consulting 9

12 Table 2.3 NERA BU-LRIC Model Results Per MMS Termination MMS Termination, per message costs 2G Cellcom Partner Pelephone Average G Cellcom Partner Pelephone Average Data Termination The LRIC estimates for data termination for Cellcom, Partner, and Pelephone are shown below. The figures and table also show the simple average cost of the three modeled operators. Figure 2.7 NERA BU-LRIC Model Results Per MB Data Termination 2G Average 60.0 Agorots per data Mbyte Average NERA Economic Consulting 10

13 Figure 2.8 NERA BU-LRIC Model Results Per MB Data Termination 3G Average Agorots per data Mbyte Average Table 2.4 NERA BU-LRIC Model Results Per MB Data Termination Data Termination, per MB costs 2G Cellcom Partner Pelephone Average G Cellcom Partner Pelephone Average Recommendation Regarding Termination Rates Based on the costs detailed above, NERA s recommendation for mobile termination rates is explained in the following sub-sections. NERA Economic Consulting 11

14 Symmetric rate We recommend that MOC set a single symmetric MTR for the three operators included in the study, calculated as a simple average. This approach is consistent with the LRIC modeling principle that the costs of an efficient entrant operator are used to set MTRs. Furthermore, it is consistent with previous MTR regulation in Israel, and the Common Position of the European Regulators Group. 1 We further recommend that this single rate is set on the basis of a blend of the costs of 2G and 3G termination. All three Israeli operators modeled have both 2G and 3G operations, and this assumption is arguably conservative, because while the costs of service provision using 3G technology are lower than those using 2G technology, it is likely that any new entrant operator would choose only to deploy a 3G network, and to use national roaming for the provision of 2G services where necessary Network externalities and other mark-ups In its consultation response one operator argued in favor of a mark-up to allow for network externalities. A network externality is the benefit accruing to existing network subscribers when a new subscriber joins the network, as a result of the fact that calls can now be made to and from that new subscriber. From an economic perspective the socially optimal level of mobile penetration is such that the marginal social cost of a new subscriber joining a mobile network is equal to the marginal social benefit, and not just the marginal private benefit. Consequently it has sometimes been argued that the socially optimal price of mobile subscription should include a subsidy in order to attract marginal subscribers into joining mobile networks, and that this should be funded by a mark-up on MTRs. However, very few regulatory authorities in developed countries permit a network externality mark-up. Furthermore, and as noted during the previous regulation of MTRs in 2004, the required value of such an externality mark-up is difficult to quantify, and there are important reasons to believe that in Israel the value of the mark-up would be negligible. This is primarily because the level of mobile penetration in Israel is already very high meaning that the number of non-subscribers who would be sensitive to a reduced subscription charge funded by a network externality mark-up must be very limited. Given that penetration has increased to over 120% since the last regulatory period, this argument has strengthened and NERA sees no justification for a network externality mark-up. Regarding other mark-ups, one operator asserted that retail costs and sales and marketing costs to be included in the MTRs. However, these costs are not network costs and are not relevant to interconnection services, and consequently are not considered in the MTR recommendation. 1 See common_position_on_mtrftr_symmetry.pdf. NERA Economic Consulting 12

15 Glide paths The costs of mobile termination calculated by the revised model are shown in Figure 2.9 below, along with the current MTRs, and NERA s glide path recommendation. It is clear that the current Israeli MTRs are considerably in excess of the NERA cost estimates, and we note that had the trend in MTR price reductions between 2005 and 2008 (when the previous price control period came to an end) continued, the 2010 MTR would be around 15 agorot per minute. As a consequence of the large discrepancy between the current MTRs in force and the costs of mobile termination in Israel we recommend a glide path between the current MTR and the costs calculated in this study. Figure 2.9 NERA voice glide path recommendation (real 2009 agorot) 40 Cost of mobile termination Current MTR NERA cost calculation NERA recommendation Source: NERA. Note that the current MTRs have been converted to 2009 prices using CPI data. It is NERA s view that a glide path is required because it strikes an appropriate balance between maximizing consumer benefit and minimizing industry disruption. An immediate movement to the newly calculated cost of mobile termination might at first sight seem to maximize consumer benefit by aligning price with cost. However, this fails to consider the potential disruption caused to mobile operators by the MTR reduction and any responses they may make in terms of cutting costs or increasing retail prices. In order to allow the mobile operators time to reduce their costs, and provide them with an incentive to do so as quickly as possible, we recommend that mobile termination rates fall in stages from their current level to reach the costs of mobile termination in 2012, and then follow the cost path to For 2010 we recommend an MTR of 13.5 agorot per minute, based on the previous trend explained above plus a limited further downward adjustment. For 2011 we NERA Economic Consulting 13

16 recommend an MTR of 10 agorot per minute to allow for a linear reduction down to the actual level of costs calculated by the revised model of 6.34 agorot per minute in In 2013 and 2014 the costs calculated by the revised model of 5.91 agorot and 5.55 agorot per minute respectively should be applied. All these figures are in 2009 prices and exclude royalties. This recommendation is summarized in Table 2.4 below. Table 2.4 Summary Recommendation Israeli Voice Termination Rates (real 2009 ILS agorot and excluding royalties) Voice cost (per minute) Voice price (per minute) Source: NERA. For the same reasons as those stated above in the context of voice termination we also recommend a glide path for SMS termination rates, as shown in Figure 2.10 below. Figure 2.10 NERA SMS glide path recommendation (real 2009 agorot) 6 Cost of SMS termination Current SMS rate NERA cost calculation NERA recommendation Source: NERA. Note that the current MTRs have been converted to 2009 prices using CPI data. For 2010 we recommend a rate of 1.53 agorot per minute, calculated on the basis of the same percentage reduction relative to the prevailing price as for voice termination. For 2011 we recommend a rate of 0.84 agorot per minute to allow for a linear reduction down to the actual level of costs calculated by the revised model of 0.15 agorot per minute in In 2013 and NERA Economic Consulting 14

17 2014 the costs calculated by the revised model of 0.14 agorot and 0.13 agorot per minute respectively should be applied. All these figures are in 2009 prices and exclude royalties. This recommendation is summarized in Table 2.5 below. Table 2.5 Summary Recommendation Israeli SMS Termination Rates (real 2009 ILS agorot and excluding royalties) SMS cost (per minute) SMS price (per minute) Source: NERA. NERA Economic Consulting 15

18 3. New Data and Model Revisions During the consultation process the operators raised a variety of points concerning the model and in many cases provided new data that had not previously been made available to us, despite being expressly requested in our data request. Reflecting this, changes have been made to the model. These changes and the reasons for them are described below. Further detailed responses to all points raised by the operators are contained in the individual responses in Appendix B (for Cellcom), Appendix C (for Partner), and Appendix D (for Pelephone). In those instances where the model input data have been changed as a result of new information, or the revised model contains modifications compared to the draft model, we indicate the impact of the changes on voice termination costs Summary of Model Revisions The impacts of the revisions to the model are shown in Table 3.1 below. The effects of the changes are shown in a cumulative manner. They relate to the 2010 costs of mobile termination, expressed in real 2009 terms and excluding a mark-up for royalties. Since the results are shown for the blended costs of all three modeled operators, the impacts shown will differ from those for the individual operators. In addition, since the changes are displayed in a cumulative way, making changes individually or in a different order may lead to some differences in the size of the different effects. However the total impact of all the changes will be the same irrespective of the order in which they are carried out. Table 3.1 Summary of Model Revisions (real 2009 agorot per minute, no royalties) Description 2010 result Change Model sent for consultation G licence fee exchange rate Working capital uplift New traffic split Backhaul adjusted to 50% MW and 50% LL Bouncing busy hour and busy day percentage Carrier capacity, shared carrier resouces and dimensioning Site opex G licence fee Cell radii calibration Capex Traffic volumes Peak traffic allocation Cell capacity Opex percentages NERA Economic Consulting 16

19 The steps involved in modifying the results of the draft model to reach those in the revised model are shown in Figure 3.1 below. Starting with the model sent for consultation changes are made in the order specified in the figure from left to right. The vertical axis shows the cumulative change in 2010 cost of termination. Figure 3.1 Summary of Model Revisions on 2010 costs (real 2009 agorot per minute, no royalties) 2010 cost of termination (real 2009 agorots) Model sent for consultation 3G licence fee exchange rate Working capital uplift New traffic split Backhaul adjusted to 50% MW and 50% LL Bouncing busy hour and busy day percentage Carrier capacity, shared carrier resouces and dimensioning Site opex 2G licence fee Cell radii calibration Capex Traffic volumes Peak traffic allocation Cell capacity Opex percentages The modifications made are explained below Operational Expenditures New data for site opex Comments were received from all operators on the subject of site opex, and they all provided data during the consultation process. This contrasts with the situation when the draft model was constructed, when only one usable data point for site opex had been provided by the operators. This was of NIS [ ] per annum per site in However, in response to the consultation the operator which submitted this information made a change to its declaration to a considerably higher figure. Now that NERA has received site opex data from all operators we have analyzed and compared the submissions and revised the site opex input data used in the draft model. NERA Economic Consulting 17

20 In analyzing the different sets of site opex figures received from the operators we considered the type of site or sites (for example macrosites, smaller sites or some average site opex figure) for which data had been provided, and the time period to which the submissions related. All three modeled operators provided breakdowns of the costs into sub-categories such as rent and electricity, enabling us to compare the components of costs declared by the different operators and ensure like for like comparisons. This process revealed that the figures provided by one operator were outliers, and that the remaining figures were comparable, both in total and at a disaggregated level. For example, site rental costs were consistently in a range of around NIS [ ]to [ ] per annum, with electricity at around NIS [ ]to [ ] per annum. This reinforced our view that it was appropriate to exclude the outlying data, which appeared to be consistently in excess of market prices. This left three estimates in a range of around NIS [ ] to [ ] per annum per site, and we took a simple average which resulted in a figure of NIS 110,000. To this we added an allowance for common costs of 12.5% to result in site opex of NIS 123,750 per annum per site New data on site opex trend One operator provided a comment regarding the trend in site opex, for which we had previously received no information from any of the operators. Based on this submission, which we consider to be reasonable, we have amended the site opex trend to +2.2% per annum in real terms. Both this change in the trend and the new data on site opex (see Section 3.2.1) are reflected in the Site opex change shown in Table 3.1 and Figure 3.1 above New data on other network equipment opex In the light of operator comments and data we have now received on total network opex, we have revisited the percentage mark-ups used to calculate direct and indirect opex for network elements. The new data, which are not publicly available and were not previously available to us despite having been expressly requested in our data request, are explained in the relevant appendix below. Taking account of these new data and allowing for common costs we have revised the opex percentages to 12.5% for radio equipment and 10% for other categories of assets. The impact of this change is shown in Table 3.1 and Figure 3.1 as Opex percentages. 2 This 12.5% mark-up for common costs is based on new figures provided by one of the operators which showed total attributable retail opex, total attributable network opex and common opex. Common opex represented 12.5% of total retail opex plus total network opex. NERA Economic Consulting 18

21 3.3. Spectrum and Licence Fees G spectrum and licence fees Comments were received from two operators regarding 2G spectrum and licence fees and the appropriate figures to use in the model. For the draft model we did not have verified data for 2G fees, but now we have comprehensive information from the operators which has been confirmed by the MOC. NERA agrees with the operators that relevant licence costs should be included in the model, and now that data have been provided and we have discussed and verified the figures with MOC, we have included 2G fees in the revised model. A wide disparity exists between the 2G fees submitted by the operators in their comments. One operator paid NIS 1.4bn in 1998, one claims NIS 2.04bn 3 and another paid nothing for its 2G spectrum and licence. We have analyzed the figures and considered two different approaches, one forward-looking and the other an accounting-based calculation. Under our forward-looking approach we base the 2G fee for all three operators on the prices paid in 2001 and 2004 for additional spectrum, updated to 2009 prices. This results in a 2G fee of NIS 310,274,217 per operator, which is depreciated over a 20 year asset life. 4 The alternative accounting-based calculation considers the historical sums paid by the operators and applies the cost of capital to the written down value of the NIS 1.4bn licence acquired in Comparison of the two methods shows that there is very little difference between the cost calculations, once a simple average is taken to produce a blended rate for the three modeled operators. Reflecting this analysis we have implemented the forward looking approach in the model, and use a 2G spectrum and licence fee of NIS 310,274,217 for all modeled operators, depreciated over a 20 year term. This change is shown as 2G licence fee in Table 3.1 and Figure 3.1 above. This change also includes an adjustment to the frequency fees for each operator, which had been applied to the incorrect operators in the draft report. The impact of this is a reduction in the costs of termination of 0.01 agorot per minute in G spectrum and licence fees The misapplication of the exchange rate used to calculate 3G licence fees in the draft model is acknowledged and has been corrected in the revised model The impact of this change is very small. In addition, in response to comments from one operator we have accounted for inflation by updating the prices of 3G licences purchased in 2001 to 2009 prices. Both these changes fall into the 3G licence fee exchange rate category in Table 3.1 and Figure 3.1). 3 Although we note that in the 2004 model Analysys used a figure of $7,252,986 for the 850 spectrum. 4 This asset life matches the lives of the 3G licences, and is, in our view, reasonable given that although 2G services may be turned off during this time, the spectrum could be refarmed. This is also consistent with an assumption made by one of the operators in its own bottom-up model. NERA Economic Consulting 19

22 3.4. Network Dimensioning G carrier capacity The draft model employs finite capacity limits (see Section below). These limits are imposed by code-tree availability, and power considerations and constrained by the overall data rate achievable in aggregate in a fully loaded cell. In turn, the power resource is, itself, constrained by traffic densities and cell size. The cell dimensioning process takes account of capacity limits, including call radius and traffic density; together, these determine the contribution to carriage of the network traffic that a fully-loaded cell can provide. The key capacity parameters, which were provided by only some operators and included in the draft model were: Simultaneous calls Aggregate average data rate 69 calls 4.5 Mbps The operators submitted revised or new data for carrier capacity. Two operators suggested revising downwards their capacity estimate for simultaneous calls. In one case the operator had misunderstood our data request and provided a figure of [ ] on the basis of the code-tree allocation; in the other case, the operator stressed the importance of the soft handover process. One operator suggested [ ] concurrent calls, another suggested [ ]. The figure of [ ] calls is close to the assumption of 29 Erlangs stated by Ofcom in its 2010 model report. 5 The available codes could be all used for concurrent calls in the absence of soft handover. However, during the process of soft handover a call occupies one capacity unit in one cell, and one capacity unit in another cell. This reduces, in effect, the number of simultaneous calls that can be handled by a cell, because part of a cell s capacity is being used by calls also handled by another cell. Notwithstanding this, equipment vendor documentation 6 and one of the Israeli operators previously told us that HSPA does not support soft handover, and the draft version of the model therefore allocated all the available codes for voice, where demanded. However, neither the documentation nor the operator s comment related to whether soft handover is, or is not, supported on shared HSPA carriers where data transmission is also occurring. 5 See 29 Erlangs is consistent (at a 1% blocking rate, whereas Ofcom employs 2% in its model) to a cell with capacity to carry 40 simultaneous calls. 6 See, for example, Ericsson, Basic Concepts of HSPA, p12, The dedicated uplink and downlink channels of Release 99 use soft handover, while HS-DSCH does not. Implementing soft handover (which, by definition, implies multiple base stations) for the high-speed channels is not feasible, since fast channel-dependent scheduling is handled by a single base station. [link broken] Archived at: Also Qualcomm WCDMA Network Planning and Optimisation, p8, Since HSDPA does not make use of soft handover, it is of the utmost importance that a single dominant server be available in the majority of the network. NERA Economic Consulting 20

23 The operator which mentioned the need for an allowance for soft handover and suggested a capacity of [ ] concurrent calls assumed a [ ] loss for soft handover. However, in a separate communication this operator admitted that handsets using HSPA would not be in soft handover, which calls into question the system efficiency of making any allowance for soft handover on HSPA carriers. Looking at precedents outside Israel, the Ofcom model makes allowance for soft handover while assuming the use of shared HSPA carriers, though the rationale for this is not explained. The Ofcom model employs a less aggressive allowance for soft handover of 30%, consistent with its capacity assumption of there being 29 Erlangs on 40 channels. In the light of conflicting evidence, we have concluded that it would be prudent to have the option in the model to use a carrier capacity of either 69 calls (with no soft handover) or 40 calls (with soft handover). One operator also queried the capacity assumption for data on an HSPA carrier, saying that it should be higher. The draft model employed a carrier data capacity of 4.5Mbps, a figure previously provided by a different Israeli operator. Again looking at at outside precedent, the Ofcom model in the UK employs a lower figure for the carrier capacity, of 2.14Mbps. NERA has extended the model to allow selection of either the original 4.5 Mbps capcity for an HSPA carrier, or the value of 2.14 Mbps capacity as employed in the Ofcom model. The changes to the number of simultaneous calls and the data capacity of an HSPA carrier are both is included in the Cell capacity change in Table 3.1 and Figure 3.1 above Daily traffic profile One operator provided new survey evidence on the daily and monthly traffic profile for voice and data. In NERA s opinion the model should reflect local traffic characteristics, and this newly submitted evidence has been taken into account. The profiles show that the busiest time of day is around 5 pm when 7.4% of a busy day s voice traffic is carried, together with 5.7% of the day s data traffic. The data also showed that 0.316% of a year s voice traffic occurred in the busy day, and that data usage was fairly level throughout the week (including rest days), suggesting 362 busy days per year for data activity. The revised model adopts these parameters, which are included in the Bouncing busy hour and busy day percentage change shown in Table 3.1 and Figure 3.1 above. For discussion of the bouncing busy hour see Section below. 7 We note that changing the number of circuits from 69 to 40 increases voice termination costs whereas changing the aggregate average data rate from 4.5 Mbps to 2.14 Mbps decreases voice termination costs. The latter is true because changing the average data rate from 4.5 Mbps to 2.14 Mbps means that 1 Mb of data is worth more minutes and so a higher proportion of (fixed) costs are allocated to data and consequently less to voice. The net effect of these two changes taken together is that the termination costs fall as shown in Table 3.1 under the heading Cell capacity. NERA Economic Consulting 21

24 Treatment of 3G voice and data Simultaneous voice and data demand Some operators raised questions about the draft model s treatment of simultaneous voice and data demands. The draft model took account of voice and data demand and provisioning, using system assumptions about the resource allocation in an HSPA carrier. The draft model computed sufficient carriers for voice and data in the busy hour, on the basis that, in HSPA, the WCDMA 'code tree' (the resource on the carrier) is separated into voice codes and data codes that are simultaneously available. 8 Voice traffic from current handsets cannot use HS codes; hence the HS codes represent a separate resource that is reserved for data. 9 Non-HS codes would, in effect, be reserved for voice. Therefore, demands for voice or data do not 'eat into' each other's capacity. At a system level the resources needed are those that are needed for voice, or those that are needed for data, whichever is the higher. In their responses, two operators suggested that carriers can be configured to prioritize voice over data, implying that the HS codes could be switched off. Vendor documentation suggests that HS codes could indeed be switched off but only semi-statically and not dynamically. 10 We have consulted with an operator in another country which has advised us that, while that used to be the case, in release 7 the HS allocation can be dynamically assigned. In these circumstances voice and data can be considered to dip into a pool of common code resources. Given a common resource pool, voice and data demands are added to estimate a combined demand to be met from the carriers. In light of the discrepancy between equipment vendor technical documentation and the apparent recent change in release 7, NERA considers it prudent to include both the capacity estimation methods described above and allow the user to choose between them. The revised model therefore includes voice and data provisioning algorithms for both: Separate semi-static allocations for HS and non-hs codes, and Dynamic code assignment requiring summation of voice and data traffic In deriving its revised cost estimates NERA has used the shared carrier resources option of dynamic code assignment described above. 8 Data codes are assumed to be high speed (HS) codes at SF16, and voice codes were non-hs at SF Data can use non-hs codes but an operator would not configure the carrier to be used this way because to do so would drastically lower the carrier s capability to serve the total data demands. 10 HS codes are described in Telenor s technical journal, Teletronikk, in an article titled HSPA and LTE Futureproof MobileBroadband solutions, Figure 8, page The article describes the use of HS codes for high speed data communication. A somewhat more informative chart (citing Qualcomm s HSPA protocol and physical layer documentation), illustrating the allocation of the practical maximum of 15 HS codes and showing that voice traffic uses non-hscodes at SF=128, can be found on slide 64 of Dr Shah s HSPA training presentation. NERA Economic Consulting 22

25 Voice and data equivalence The equivalence of voice traffic and data traffic in terms of carrier usage depends on assumptions about cell throughput. The figures employed for cell throughput in the draft model were provided by an Israeli operator for a typical HSPA cell, without MIMO, fully loaded with traffic, with realistic propagation and interference expectations. At lighter traffic loads, or in atypical cell radio conditions, a much higher data rate could be achieved. However, networks are dimensioned to handle busy hour traffic, and operators will require as many cell sites as are necessary to meet that demand. We have therefore not changed the methodology for determining voice and data equivalence from that used in the draft model. However, the option of using either NERA s initial assumptions for HSPA capacity, or the lower capacity option (the shared carrier resources option) described above has an impact on the voice and data equivalence in the revised model. The changes to the calculation of simultaneous voice and data demand and the impact this has on voice and data equivalence are included in the Carrier capacity, shared carrier resources and dimensioning change in Table 3.1 and Figure 3.1 above Allocation of costs according to busy hour or total traffic One operator submitted that costs should be allocated between services on the basis of busy hour traffic volumes rather than total traffic volumes, and provided new data (see Section 3.4.2). The rationale for this is that the network is dimensioned according to peak traffic demand, so costs should be allocated in a manner that reflects this cost causation process. We note that this is not a standard feature of LRIC models produced in other jurisdictions, and, for example, is not included in the most recent Ofcom model. Furthermore, the submission of another operator suggests that it views the allocation of costs on a total (24 hour) traffic basis as being correct. In previous models, data traffic levels were low and there was no information on differences in the time of day pattern of voice and data traffic, so no issues regarding cost allocation arose. However, it is NERA s view that, given the growth of data during the period under consideration and the availability of information on differences in the time of day pattern of voice and data traffic, the allocation of costs on the basis of peak traffic demands is correct from an economic perspective. Now that we have been provided with survey data of time of day traffic profiles for voice and data we have implemented a change in the revised model to allocate costs on the basis of peak traffic volumes and not total traffic volumes. The impact of this is shown in Table 3.1 and Figure 3.1 above as Peak traffic allocation Bouncing busy hour NERA agrees that a bouncing busy hour exists in mobile networks. Adjustments for the bouncing busy hour or mobility effect can be made by either adjusting: Utilisation of BTSs/NodeBs, and backhaul, or NERA Economic Consulting 23

26 Uplifting the traffic demands on the BTSs/NodeBs and backhaul In the draft model NERA considered that its utilization assumption of 70% for BTSs and NodeBs provided sufficient headroom for the bouncing busy hour effect. In response to the consultation we were provided with two estimates by operators of the ratio of bouncing busy hour demand to busy hour demand. In one case the ratio was very high and, when combined with the generous utilization assumption of 70% for BTSs and Node Bs, could be expected to overstate costs. The other estimate showed a ratio of [ ]. In order to be certain that the bouncing busy hour is fully taken into account an additional uplift of 11% for the bouncing busy hour has been introduced. This is added to voice and MMS only; other data services do not have a dynamic effect on the radio network, and SMS, for example, does not place a load on the radio network. This 11% uplift is applied to BTS/NodeB requirements. This option may be selected by the model user via the Bouncing busy hour uplift switch on the input sheet. As explained in Section 2, we have applied this uplift when deriving our new cost estimates, and the impact on the results is included in the Bouncing busy hour and busy day percentage change in Table 3.1 and Figure 3.1 above Backhaul and Core Transmission BTS-BSC/Node B-RNC links The draft model assumed, based on experience elsewhere, that the efficient provision of BTS- BSC and Node B-RNC links would be via microwave. Having reviewed the new and detailed survey based information provided to us as part of the consultation process, we have changed the base case assumption in the revised model to allow for 50% microwave transmission and 50% leased line transmission. The impact of this change is shown as Backhaul adjusted to 50% MW and 50% LL in Table 3.1 and Figure 3.1 above Core network transmission On the related topic of core network transmission, two operators commented that their actual self-provided transmission infrastructure and costs should be taken into account. However, it was also argued by one of these operators that the Israeli leased line market is competitive. It is NERA s view that in a competitive market the cost of leased lines would be less than or equal to those of self-provision, particularly given the economies of scale available to Bezeq. Consequently, the efficient choice by a hypothetical entrant operator would be to use leased lines for core transmission, as was assumed in the base case of the NERA draft model. NERA Economic Consulting 24

27 3.6. Demand Population The population forecasts in the draft model were based on historical data from the Israeli Central Bureau of Statistics (CBS). The average population growth rate (1.78% p.a.) was used to forecast the population of Israel in future years. During the consultation process one of the operators noted that these forecasts were slightly above official forecasts produced by CBS in The 2008 CBS forecasts come in three variants which are presented in Table 3.2 below. Using the growth rates implied by these CBS forecasts, Table 3.3 presents CBS population forecasts for the years 2009 through Table 3.2 CBS Population Forecasts Three Variants 11 Year End Figures ('000) Implied Annual Growth Rate Low Variant % 1.42% Medium Variant % 1.53% High Variant % 1.72% Table 3.3 CBS Population Forecasts Three Variants ( ) Year End Figures ( 000) Low Variant , , , ,995.3 Medium Variant , , , ,051.5 High Variant , , , ,157.4 Source: NERA calculations Updated population data from CBS, estimates that the Israeli population was 7,546.1 thousand in December and 7,602.4 thousand in May Comparing the CBS forecasts (made in 2008) in Table 3.3 with theses updated population estimates it can be seen that the high variant that comes closest to reality - though even the high variant underestimates Israeli population in 2009 and is likely to do so in 2010 as well. Given this, one would be justified in 11 Sources: Table 5, High variant - Components of population growth by population group and religion ( Table 6, Medium variant - Components of population growth by population group and religion ( and Table 7, Low variant - components of population growth by population group and religion ( 12 CBS, Time Series DataBank, Population - Population, By Population Group - Population De Jure (From 2009 based on the 2008 Census), Published 07/06/ From the CBS website visited on 3 August 4, NERA Economic Consulting 25

28 using a slightly higher growth rate than the annual growth rate estimated by CBS in the high variant case (1.72%), as was done in the NERA draft model. However in the revised model we have used the official high variant forecast made by CBS Penetration rates and data usage New information on the growth of data usage in Israel was provided by one of the operators in response to the consultation. This information was not provided during the initial data submission process even though it was requested. Based on this new information, the revised model uses the penetration rates and data usage volumes given in Table 3.4. Table 3.4 Mobile Penetration and Data Usage Revised Model Mobile Penetration ( Handsets plus Data Cards) 126.6% 128.4% 130.0% 131.7% 133.2% 134.5% Mobile Penetration ( Handsets) 123.3% 123.8% 124.1% 124.3% 124.4% 124.5% Average data usage across all subscribers (handsets and data cards) in MB per month Source: Revised NERA Model We note that the handset penetration rate is close to saturation and the main source of growth in subscriber numbers is the take-up of data cards. The growth in average data usage per subscriber in Table 3.4 above is mainly driven by the forecast increase in the number of data card subscribers and higher data usage per subscriber for 3G and data card subscribers On-net/off-net traffic balance In the draft model, the split of voice traffic volumes (minutes and successful calls) among different categories of calls such as mobile on-net calls, mobile off-net calls and so on for all operators was based on data from one operator which provided a full set of data during the initial data submission process. Reflecting discussions during the consultation process and taking advantage of some further data that has now been made available to us, the revised model uses voice traffic volumes for different categories of calls provided by each operator (where such data are available) to estimate its split of voice traffic among different categories of calls. This better reflects the scope of each operator s operations. The combined effect of these amendments to the demand data are shown as the Traffic volumes change in Table 3.1 and Figure 3.1 above. 14 Some increase in the data usage per 2G subscribers is also assumed but heavy users are assumed to migrate to 3G. NERA Economic Consulting 26

29 3.7. Other Issues Traffic data In its consultation response, one operator explained that it had supplied incorrect information on the split of its traffic between 2G and 3G. It has now supplied correct data and these have been used in the revised model. The impact of this change is shown as New traffic split in Table 3.1 and Figure 3.1 above Royalties One operator commented that the royalty rates were not applied to the final results of the draft model, and another that the rate should be increased from those shown in Table 3.21 of the draft report to higher figures being considered by the Israeli government. We have discussed this issue with the MOC, and it was decided that, given the uncertainty surrounding future royalty rates, we would exclude them from our cost calculations (i.e. they have been set to zero in the model). In order to take royalty payments into account, when determining the actual termination rate to apply in each year the MOC will add the correct royalty rates as they turn out (in a similar manner to inflation rates). The modeled costs presented in this report for future years are therefore in 2009 real terms and excluding royalties. This way of treating royalty payments means there is no impact on the final results of the revised model Working capital One operator commented that a working capital uplift should be applied to the results to reflect the fact that interconnection revenue is received some time after the service is delivered. 15 NERA agrees with this comment and has implemented this in the revised model. We assume that, on average, wholesale revenue is received 45 days after the cost of handling the billed traffic was incurred, comprising 15 days for billing, and 30 days for payment. This is calculated using the following formula: Working capital uplift = *WACC 365 The impact of this is shown in Table 3.1 and Figure 3.1 as Working capital uplift WACC Comments were received from all operators regarding WACC, and some alternative calculations were presented. Having reviewed these calculations NERA feels that it is not 15 We note that implementing this does not require a corresponding adjustment to reflect the fact that the operator might not pay interconnection revenue to other operators immediately after services are delivered. This is because a delay in receipt of interconnection revenues represents a network cost, whereas revenues it owes to other operators are a retail cost. NERA Economic Consulting 27

30 necessary to change its calculations, and detailed responses to the individual operator comments are provided in the relevant Appendices Capex New or revised capex data have been provided by the operators as part of the consultation process. NERA considers most of these to be reasonable estimates of capex costs for efficient assets, and they have been taken into account in the revised model (see the Capex item in Table 3.1 and Figure 3.1 above). The revised model includes adjustments to the radio site capex, and purchase costs of CDMA MSC, STP, HLR, VMS, pre-paid systems, data network IP equipment, and NMS systems. New data, for purchase costs of Node Bs, were received from one operator. The costs were substantially higher than those received from other operators and so were rejected as not representing efficient purchase prices. Other capex data were provided for ringtone download equipment, and for roaming interworking gateways; both of these were rejected for inclusion in the revised model. The ringtone download equipment was not included in the cost analysis because this equipment is used for retail services. Roaming interworking equipment was described by the operator as required for Israeli customers roaming elsewhere. Since the costs of handling that traffic are outside the scope of the analysis, this capex was also not included IP network costs Following comments from two operators and the provision of new data, IP core networks costs have been added to the model. However, insufficient information was provided to allow us to develop a dimensioning process. We therefore had to devise a system for including such costs. 16 These costs are categorised as GGSN costs and are allocated to data services, so have no impact on voice termination Cell radii In their responses to the consultation, the operators made various comments regarding cell radii and the relationship between cell radii and BTS and Node B quantities in the model. In reality and in complete contradiction to what was claimed by the operators, the draft model produced estimates of base station numbers that were close to reality (see Section below). 17 In calibrating the revised model we have made an adjustment to the 3G 2100 traffic cell radii in order to more closely approximate the numbers of Node Bs actually deployed (which would otherwise be overstated). We now assume 1.5km radius in urban areas, 2.2km in suburban 16 Based on consistent estimates of around NIS 12m in total, network elements with costs of NIS 6m each have been added to the 2G and 3G dimensioning sheets. We assume 100% utilisation, a 7 year asset life, and unit price and opex trends of 0% in order to produce costs of NIS 12m in each year. 17 In the case of one operator the combined total of base stations was reasonably closely approximated by the model but the balance between 2G and 3G was wrong, mainly reflecting the incorrect traffic split information provided by the operator. NERA Economic Consulting 28